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2077 lines
522 KiB
2077 lines
522 KiB
2 years ago
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"cells": [
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"___\n",
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"\n",
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"<a href='https://www.udemy.com/user/joseportilla/'><img src='../Pierian_Data_Logo.png'/></a>\n",
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"___\n",
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"<center><em>Copyright by Pierian Data Inc.</em></center>\n",
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"<center><em>For more information, visit us at <a href='http://www.pieriandata.com'>www.pieriandata.com</a></em></center>"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Random Forest - Classification"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## The Data\n",
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"\n",
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"We will be using the same dataset through our discussions on classification with tree-methods (Decision Tree,Random Forests, and Gradient Boosted Trees) in order to compare performance metrics across these related models.\n",
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"\n",
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"We will work with the \"Palmer Penguins\" dataset, as it is simple enough to help us fully understand how changing hyperparameters can change classification results.\n",
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"\n",
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"\n",
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"<img src=\"penguin.jpg\" style=\"max-width:400px\">\n",
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"\n",
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"Data were collected and made available by Dr. Kristen Gorman and the Palmer Station, Antarctica LTER, a member of the Long Term Ecological Research Network.\n",
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"\n",
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"Gorman KB, Williams TD, Fraser WR (2014) Ecological Sexual Dimorphism and Environmental Variability within a Community of Antarctic Penguins (Genus Pygoscelis). PLoS ONE 9(3): e90081. doi:10.1371/journal.pone.0090081\n",
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"\n",
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"Summary:\n",
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"The data folder contains two CSV files. For intro courses/examples, you probably want to use the first one (penguins_size.csv).\n",
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"\n",
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"* penguins_size.csv: Simplified data from original penguin data sets. Contains variables:\n",
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"\n",
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" * species: penguin species (Chinstrap, Adélie, or Gentoo)\n",
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" * culmen_length_mm: culmen length (mm)\n",
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" * culmen_depth_mm: culmen depth (mm)\n",
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" * flipper_length_mm: flipper length (mm)\n",
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" * body_mass_g: body mass (g)\n",
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" * island: island name (Dream, Torgersen, or Biscoe) in the Palmer Archipelago (Antarctica)\n",
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" * sex: penguin sex\n",
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"\n",
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"* (Not used) penguins_lter.csv: Original combined data for 3 penguin species \n",
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"\n",
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"Note: The culmen is \"the upper ridge of a bird's beak\" \n",
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"\n",
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"**Our goal is to create a model that can help predict a species of a penguin based on physical attributes, then we can use that model to help researchers classify penguins in the field, instead of needing an experienced biologist**"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Imports"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 77,
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import pandas as pd\n",
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"import matplotlib.pyplot as plt\n",
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"import seaborn as sns"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 78,
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"metadata": {},
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"outputs": [],
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"source": [
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"df = pd.read_csv(\"../DATA/penguins_size.csv\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 79,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>species</th>\n",
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" <th>island</th>\n",
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" <th>culmen_length_mm</th>\n",
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" <th>culmen_depth_mm</th>\n",
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" <th>flipper_length_mm</th>\n",
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" <th>body_mass_g</th>\n",
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" <th>sex</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>Adelie</td>\n",
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" <td>Torgersen</td>\n",
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" <td>39.1</td>\n",
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" <td>18.7</td>\n",
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" <td>181.0</td>\n",
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" <td>3750.0</td>\n",
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" <td>MALE</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>Adelie</td>\n",
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" <td>Torgersen</td>\n",
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" <td>39.5</td>\n",
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" <td>17.4</td>\n",
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" <td>186.0</td>\n",
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" <td>3800.0</td>\n",
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" <td>FEMALE</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>Adelie</td>\n",
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" <td>Torgersen</td>\n",
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" <td>40.3</td>\n",
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" <td>18.0</td>\n",
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" <td>195.0</td>\n",
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" <td>3250.0</td>\n",
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" <td>FEMALE</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>Adelie</td>\n",
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" <td>Torgersen</td>\n",
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" <td>36.7</td>\n",
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" <td>19.3</td>\n",
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" <td>193.0</td>\n",
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" <td>3450.0</td>\n",
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" <td>FEMALE</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>5</th>\n",
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" <td>Adelie</td>\n",
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" <td>Torgersen</td>\n",
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" <td>39.3</td>\n",
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" <td>20.6</td>\n",
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" <td>190.0</td>\n",
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" <td>3650.0</td>\n",
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" <td>MALE</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" species island culmen_length_mm culmen_depth_mm flipper_length_mm \\\n",
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"0 Adelie Torgersen 39.1 18.7 181.0 \n",
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"1 Adelie Torgersen 39.5 17.4 186.0 \n",
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"2 Adelie Torgersen 40.3 18.0 195.0 \n",
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"4 Adelie Torgersen 36.7 19.3 193.0 \n",
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"5 Adelie Torgersen 39.3 20.6 190.0 \n",
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"\n",
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" body_mass_g sex \n",
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"0 3750.0 MALE \n",
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"1 3800.0 FEMALE \n",
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"2 3250.0 FEMALE \n",
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"4 3450.0 FEMALE \n",
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"5 3650.0 MALE "
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]
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},
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"execution_count": 79,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"df = df.dropna()\n",
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"df.head()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Train | Test Split"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 87,
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"metadata": {},
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"outputs": [],
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"source": [
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"X = pd.get_dummies(df.drop('species',axis=1),drop_first=True)\n",
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"y = df['species']"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 88,
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"metadata": {},
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"outputs": [],
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"source": [
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"from sklearn.model_selection import train_test_split"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 89,
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"metadata": {},
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"outputs": [],
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"source": [
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"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=101)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Random Forest Classification"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 90,
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"metadata": {},
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"outputs": [],
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"source": [
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"from sklearn.ensemble import RandomForestClassifier"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 91,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Help on class RandomForestClassifier in module sklearn.ensemble._forest:\n",
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"\n",
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"class RandomForestClassifier(ForestClassifier)\n",
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" | RandomForestClassifier(n_estimators=100, *, criterion='gini', max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features='auto', max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, bootstrap=True, oob_score=False, n_jobs=None, random_state=None, verbose=0, warm_start=False, class_weight=None, ccp_alpha=0.0, max_samples=None)\n",
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" | \n",
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" | A random forest classifier.\n",
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" | \n",
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" | A random forest is a meta estimator that fits a number of decision tree\n",
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" | classifiers on various sub-samples of the dataset and uses averaging to\n",
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" | improve the predictive accuracy and control over-fitting.\n",
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" | The sub-sample size is controlled with the `max_samples` parameter if\n",
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" | `bootstrap=True` (default), otherwise the whole dataset is used to build\n",
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" | each tree.\n",
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" | \n",
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" | Read more in the :ref:`User Guide <forest>`.\n",
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" | \n",
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" | Parameters\n",
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" | ----------\n",
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" | n_estimators : int, default=100\n",
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" | The number of trees in the forest.\n",
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" | \n",
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" | .. versionchanged:: 0.22\n",
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" | The default value of ``n_estimators`` changed from 10 to 100\n",
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" | in 0.22.\n",
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" | \n",
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" | criterion : {\"gini\", \"entropy\"}, default=\"gini\"\n",
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" | The function to measure the quality of a split. Supported criteria are\n",
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" | \"gini\" for the Gini impurity and \"entropy\" for the information gain.\n",
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" | Note: this parameter is tree-specific.\n",
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" | \n",
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" | max_depth : int, default=None\n",
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" | The maximum depth of the tree. If None, then nodes are expanded until\n",
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" | all leaves are pure or until all leaves contain less than\n",
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" | min_samples_split samples.\n",
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" | \n",
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" | min_samples_split : int or float, default=2\n",
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" | The minimum number of samples required to split an internal node:\n",
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" | \n",
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" | - If int, then consider `min_samples_split` as the minimum number.\n",
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" | - If float, then `min_samples_split` is a fraction and\n",
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" | `ceil(min_samples_split * n_samples)` are the minimum\n",
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" | number of samples for each split.\n",
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" | \n",
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" | .. versionchanged:: 0.18\n",
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" | Added float values for fractions.\n",
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" | \n",
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" | min_samples_leaf : int or float, default=1\n",
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" | The minimum number of samples required to be at a leaf node.\n",
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" | A split point at any depth will only be considered if it leaves at\n",
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" | least ``min_samples_leaf`` training samples in each of the left and\n",
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" | right branches. This may have the effect of smoothing the model,\n",
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" | especially in regression.\n",
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" | \n",
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" | - If int, then consider `min_samples_leaf` as the minimum number.\n",
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" | - If float, then `min_samples_leaf` is a fraction and\n",
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" | `ceil(min_samples_leaf * n_samples)` are the minimum\n",
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" | number of samples for each node.\n",
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" | \n",
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" | .. versionchanged:: 0.18\n",
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" | Added float values for fractions.\n",
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" | \n",
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" | min_weight_fraction_leaf : float, default=0.0\n",
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" | The minimum weighted fraction of the sum total of weights (of all\n",
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" | the input samples) required to be at a leaf node. Samples have\n",
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" | equal weight when sample_weight is not provided.\n",
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" | \n",
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" | max_features : {\"auto\", \"sqrt\", \"log2\"}, int or float, default=\"auto\"\n",
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" | The number of features to consider when looking for the best split:\n",
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" | \n",
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" | - If int, then consider `max_features` features at each split.\n",
|
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|
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" | - If float, then `max_features` is a fraction and\n",
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||
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" | `int(max_features * n_features)` features are considered at each\n",
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" | split.\n",
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|
|
" | - If \"auto\", then `max_features=sqrt(n_features)`.\n",
|
||
|
|
" | - If \"sqrt\", then `max_features=sqrt(n_features)` (same as \"auto\").\n",
|
||
|
|
" | - If \"log2\", then `max_features=log2(n_features)`.\n",
|
||
|
|
" | - If None, then `max_features=n_features`.\n",
|
||
|
|
" | \n",
|
||
|
|
" | Note: the search for a split does not stop until at least one\n",
|
||
|
|
" | valid partition of the node samples is found, even if it requires to\n",
|
||
|
|
" | effectively inspect more than ``max_features`` features.\n",
|
||
|
|
" | \n",
|
||
|
|
" | max_leaf_nodes : int, default=None\n",
|
||
|
|
" | Grow trees with ``max_leaf_nodes`` in best-first fashion.\n",
|
||
|
|
" | Best nodes are defined as relative reduction in impurity.\n",
|
||
|
|
" | If None then unlimited number of leaf nodes.\n",
|
||
|
|
" | \n",
|
||
|
|
" | min_impurity_decrease : float, default=0.0\n",
|
||
|
|
" | A node will be split if this split induces a decrease of the impurity\n",
|
||
|
|
" | greater than or equal to this value.\n",
|
||
|
|
" | \n",
|
||
|
|
" | The weighted impurity decrease equation is the following::\n",
|
||
|
|
" | \n",
|
||
|
|
" | N_t / N * (impurity - N_t_R / N_t * right_impurity\n",
|
||
|
|
" | - N_t_L / N_t * left_impurity)\n",
|
||
|
|
" | \n",
|
||
|
|
" | where ``N`` is the total number of samples, ``N_t`` is the number of\n",
|
||
|
|
" | samples at the current node, ``N_t_L`` is the number of samples in the\n",
|
||
|
|
" | left child, and ``N_t_R`` is the number of samples in the right child.\n",
|
||
|
|
" | \n",
|
||
|
|
" | ``N``, ``N_t``, ``N_t_R`` and ``N_t_L`` all refer to the weighted sum,\n",
|
||
|
|
" | if ``sample_weight`` is passed.\n",
|
||
|
|
" | \n",
|
||
|
|
" | .. versionadded:: 0.19\n",
|
||
|
|
" | \n",
|
||
|
|
" | min_impurity_split : float, default=None\n",
|
||
|
|
" | Threshold for early stopping in tree growth. A node will split\n",
|
||
|
|
" | if its impurity is above the threshold, otherwise it is a leaf.\n",
|
||
|
|
" | \n",
|
||
|
|
" | .. deprecated:: 0.19\n",
|
||
|
|
" | ``min_impurity_split`` has been deprecated in favor of\n",
|
||
|
|
" | ``min_impurity_decrease`` in 0.19. The default value of\n",
|
||
|
|
" | ``min_impurity_split`` has changed from 1e-7 to 0 in 0.23 and it\n",
|
||
|
|
" | will be removed in 0.25. Use ``min_impurity_decrease`` instead.\n",
|
||
|
|
" | \n",
|
||
|
|
" | \n",
|
||
|
|
" | bootstrap : bool, default=True\n",
|
||
|
|
" | Whether bootstrap samples are used when building trees. If False, the\n",
|
||
|
|
" | whole dataset is used to build each tree.\n",
|
||
|
|
" | \n",
|
||
|
|
" | oob_score : bool, default=False\n",
|
||
|
|
" | Whether to use out-of-bag samples to estimate\n",
|
||
|
|
" | the generalization accuracy.\n",
|
||
|
|
" | \n",
|
||
|
|
" | n_jobs : int, default=None\n",
|
||
|
|
" | The number of jobs to run in parallel. :meth:`fit`, :meth:`predict`,\n",
|
||
|
|
" | :meth:`decision_path` and :meth:`apply` are all parallelized over the\n",
|
||
|
|
" | trees. ``None`` means 1 unless in a :obj:`joblib.parallel_backend`\n",
|
||
|
|
" | context. ``-1`` means using all processors. See :term:`Glossary\n",
|
||
|
|
" | <n_jobs>` for more details.\n",
|
||
|
|
" | \n",
|
||
|
|
" | random_state : int or RandomState, default=None\n",
|
||
|
|
" | Controls both the randomness of the bootstrapping of the samples used\n",
|
||
|
|
" | when building trees (if ``bootstrap=True``) and the sampling of the\n",
|
||
|
|
" | features to consider when looking for the best split at each node\n",
|
||
|
|
" | (if ``max_features < n_features``).\n",
|
||
|
|
" | See :term:`Glossary <random_state>` for details.\n",
|
||
|
|
" | \n",
|
||
|
|
" | verbose : int, default=0\n",
|
||
|
|
" | Controls the verbosity when fitting and predicting.\n",
|
||
|
|
" | \n",
|
||
|
|
" | warm_start : bool, default=False\n",
|
||
|
|
" | When set to ``True``, reuse the solution of the previous call to fit\n",
|
||
|
|
" | and add more estimators to the ensemble, otherwise, just fit a whole\n",
|
||
|
|
" | new forest. See :term:`the Glossary <warm_start>`.\n",
|
||
|
|
" | \n",
|
||
|
|
" | class_weight : {\"balanced\", \"balanced_subsample\"}, dict or list of dicts, default=None\n",
|
||
|
|
" | Weights associated with classes in the form ``{class_label: weight}``.\n",
|
||
|
|
" | If not given, all classes are supposed to have weight one. For\n",
|
||
|
|
" | multi-output problems, a list of dicts can be provided in the same\n",
|
||
|
|
" | order as the columns of y.\n",
|
||
|
|
" | \n",
|
||
|
|
" | Note that for multioutput (including multilabel) weights should be\n",
|
||
|
|
" | defined for each class of every column in its own dict. For example,\n",
|
||
|
|
" | for four-class multilabel classification weights should be\n",
|
||
|
|
" | [{0: 1, 1: 1}, {0: 1, 1: 5}, {0: 1, 1: 1}, {0: 1, 1: 1}] instead of\n",
|
||
|
|
" | [{1:1}, {2:5}, {3:1}, {4:1}].\n",
|
||
|
|
" | \n",
|
||
|
|
" | The \"balanced\" mode uses the values of y to automatically adjust\n",
|
||
|
|
" | weights inversely proportional to class frequencies in the input data\n",
|
||
|
|
" | as ``n_samples / (n_classes * np.bincount(y))``\n",
|
||
|
|
" | \n",
|
||
|
|
" | The \"balanced_subsample\" mode is the same as \"balanced\" except that\n",
|
||
|
|
" | weights are computed based on the bootstrap sample for every tree\n",
|
||
|
|
" | grown.\n",
|
||
|
|
" | \n",
|
||
|
|
" | For multi-output, the weights of each column of y will be multiplied.\n",
|
||
|
|
" | \n",
|
||
|
|
" | Note that these weights will be multiplied with sample_weight (passed\n",
|
||
|
|
" | through the fit method) if sample_weight is specified.\n",
|
||
|
|
" | \n",
|
||
|
|
" | ccp_alpha : non-negative float, default=0.0\n",
|
||
|
|
" | Complexity parameter used for Minimal Cost-Complexity Pruning. The\n",
|
||
|
|
" | subtree with the largest cost complexity that is smaller than\n",
|
||
|
|
" | ``ccp_alpha`` will be chosen. By default, no pruning is performed. See\n",
|
||
|
|
" | :ref:`minimal_cost_complexity_pruning` for details.\n",
|
||
|
|
" | \n",
|
||
|
|
" | .. versionadded:: 0.22\n",
|
||
|
|
" | \n",
|
||
|
|
" | max_samples : int or float, default=None\n",
|
||
|
|
" | If bootstrap is True, the number of samples to draw from X\n",
|
||
|
|
" | to train each base estimator.\n",
|
||
|
|
" | \n",
|
||
|
|
" | - If None (default), then draw `X.shape[0]` samples.\n",
|
||
|
|
" | - If int, then draw `max_samples` samples.\n",
|
||
|
|
" | - If float, then draw `max_samples * X.shape[0]` samples. Thus,\n",
|
||
|
|
" | `max_samples` should be in the interval `(0, 1)`.\n",
|
||
|
|
" | \n",
|
||
|
|
" | .. versionadded:: 0.22\n",
|
||
|
|
" | \n",
|
||
|
|
" | Attributes\n",
|
||
|
|
" | ----------\n",
|
||
|
|
" | base_estimator_ : DecisionTreeClassifier\n",
|
||
|
|
" | The child estimator template used to create the collection of fitted\n",
|
||
|
|
" | sub-estimators.\n",
|
||
|
|
" | \n",
|
||
|
|
" | estimators_ : list of DecisionTreeClassifier\n",
|
||
|
|
" | The collection of fitted sub-estimators.\n",
|
||
|
|
" | \n",
|
||
|
|
" | classes_ : ndarray of shape (n_classes,) or a list of such arrays\n",
|
||
|
|
" | The classes labels (single output problem), or a list of arrays of\n",
|
||
|
|
" | class labels (multi-output problem).\n",
|
||
|
|
" | \n",
|
||
|
|
" | n_classes_ : int or list\n",
|
||
|
|
" | The number of classes (single output problem), or a list containing the\n",
|
||
|
|
" | number of classes for each output (multi-output problem).\n",
|
||
|
|
" | \n",
|
||
|
|
" | n_features_ : int\n",
|
||
|
|
" | The number of features when ``fit`` is performed.\n",
|
||
|
|
" | \n",
|
||
|
|
" | n_outputs_ : int\n",
|
||
|
|
" | The number of outputs when ``fit`` is performed.\n",
|
||
|
|
" | \n",
|
||
|
|
" | feature_importances_ : ndarray of shape (n_features,)\n",
|
||
|
|
" | The impurity-based feature importances.\n",
|
||
|
|
" | The higher, the more important the feature.\n",
|
||
|
|
" | The importance of a feature is computed as the (normalized)\n",
|
||
|
|
" | total reduction of the criterion brought by that feature. It is also\n",
|
||
|
|
" | known as the Gini importance.\n",
|
||
|
|
" | \n",
|
||
|
|
" | Warning: impurity-based feature importances can be misleading for\n",
|
||
|
|
" | high cardinality features (many unique values). See\n",
|
||
|
|
" | :func:`sklearn.inspection.permutation_importance` as an alternative.\n",
|
||
|
|
" | \n",
|
||
|
|
" | oob_score_ : float\n",
|
||
|
|
" | Score of the training dataset obtained using an out-of-bag estimate.\n",
|
||
|
|
" | This attribute exists only when ``oob_score`` is True.\n",
|
||
|
|
" | \n",
|
||
|
|
" | oob_decision_function_ : ndarray of shape (n_samples, n_classes)\n",
|
||
|
|
" | Decision function computed with out-of-bag estimate on the training\n",
|
||
|
|
" | set. If n_estimators is small it might be possible that a data point\n",
|
||
|
|
" | was never left out during the bootstrap. In this case,\n",
|
||
|
|
" | `oob_decision_function_` might contain NaN. This attribute exists\n",
|
||
|
|
" | only when ``oob_score`` is True.\n",
|
||
|
|
" | \n",
|
||
|
|
" | See Also\n",
|
||
|
|
" | --------\n",
|
||
|
|
" | DecisionTreeClassifier, ExtraTreesClassifier\n",
|
||
|
|
" | \n",
|
||
|
|
" | Notes\n",
|
||
|
|
" | -----\n",
|
||
|
|
" | The default values for the parameters controlling the size of the trees\n",
|
||
|
|
" | (e.g. ``max_depth``, ``min_samples_leaf``, etc.) lead to fully grown and\n",
|
||
|
|
" | unpruned trees which can potentially be very large on some data sets. To\n",
|
||
|
|
" | reduce memory consumption, the complexity and size of the trees should be\n",
|
||
|
|
" | controlled by setting those parameter values.\n",
|
||
|
|
" | \n",
|
||
|
|
" | The features are always randomly permuted at each split. Therefore,\n",
|
||
|
|
" | the best found split may vary, even with the same training data,\n",
|
||
|
|
" | ``max_features=n_features`` and ``bootstrap=False``, if the improvement\n",
|
||
|
|
" | of the criterion is identical for several splits enumerated during the\n",
|
||
|
|
" | search of the best split. To obtain a deterministic behaviour during\n",
|
||
|
|
" | fitting, ``random_state`` has to be fixed.\n",
|
||
|
|
" | \n",
|
||
|
|
" | References\n",
|
||
|
|
" | ----------\n",
|
||
|
|
" | .. [1] L. Breiman, \"Random Forests\", Machine Learning, 45(1), 5-32, 2001.\n",
|
||
|
|
" | \n",
|
||
|
|
" | Examples\n",
|
||
|
|
" | --------\n",
|
||
|
|
" | >>> from sklearn.ensemble import RandomForestClassifier\n",
|
||
|
|
" | >>> from sklearn.datasets import make_classification\n",
|
||
|
|
" | >>> X, y = make_classification(n_samples=1000, n_features=4,\n",
|
||
|
|
" | ... n_informative=2, n_redundant=0,\n",
|
||
|
|
" | ... random_state=0, shuffle=False)\n",
|
||
|
|
" | >>> clf = RandomForestClassifier(max_depth=2, random_state=0)\n",
|
||
|
|
" | >>> clf.fit(X, y)\n",
|
||
|
|
" | RandomForestClassifier(...)\n",
|
||
|
|
" | >>> print(clf.predict([[0, 0, 0, 0]]))\n",
|
||
|
|
" | [1]\n",
|
||
|
|
" | \n",
|
||
|
|
" | Method resolution order:\n",
|
||
|
|
" | RandomForestClassifier\n",
|
||
|
|
" | ForestClassifier\n",
|
||
|
|
" | sklearn.base.ClassifierMixin\n",
|
||
|
|
" | BaseForest\n",
|
||
|
|
" | sklearn.base.MultiOutputMixin\n",
|
||
|
|
" | sklearn.ensemble._base.BaseEnsemble\n",
|
||
|
|
" | sklearn.base.MetaEstimatorMixin\n",
|
||
|
|
" | sklearn.base.BaseEstimator\n",
|
||
|
|
" | builtins.object\n",
|
||
|
|
" | \n",
|
||
|
|
" | Methods defined here:\n",
|
||
|
|
" | \n",
|
||
|
|
" | __init__(self, n_estimators=100, *, criterion='gini', max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features='auto', max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, bootstrap=True, oob_score=False, n_jobs=None, random_state=None, verbose=0, warm_start=False, class_weight=None, ccp_alpha=0.0, max_samples=None)\n",
|
||
|
|
" | Initialize self. See help(type(self)) for accurate signature.\n",
|
||
|
|
" | \n",
|
||
|
|
" | ----------------------------------------------------------------------\n",
|
||
|
|
" | Data and other attributes defined here:\n",
|
||
|
|
" | \n",
|
||
|
|
" | __abstractmethods__ = frozenset()\n",
|
||
|
|
" | \n",
|
||
|
|
" | ----------------------------------------------------------------------\n",
|
||
|
|
" | Methods inherited from ForestClassifier:\n",
|
||
|
|
" | \n",
|
||
|
|
" | predict(self, X)\n",
|
||
|
|
" | Predict class for X.\n",
|
||
|
|
" | \n",
|
||
|
|
" | The predicted class of an input sample is a vote by the trees in\n",
|
||
|
|
" | the forest, weighted by their probability estimates. That is,\n",
|
||
|
|
" | the predicted class is the one with highest mean probability\n",
|
||
|
|
" | estimate across the trees.\n",
|
||
|
|
" | \n",
|
||
|
|
" | Parameters\n",
|
||
|
|
" | ----------\n",
|
||
|
|
" | X : {array-like, sparse matrix} of shape (n_samples, n_features)\n",
|
||
|
|
" | The input samples. Internally, its dtype will be converted to\n",
|
||
|
|
" | ``dtype=np.float32``. If a sparse matrix is provided, it will be\n",
|
||
|
|
" | converted into a sparse ``csr_matrix``.\n",
|
||
|
|
" | \n",
|
||
|
|
" | Returns\n",
|
||
|
|
" | -------\n",
|
||
|
|
" | y : ndarray of shape (n_samples,) or (n_samples, n_outputs)\n",
|
||
|
|
" | The predicted classes.\n",
|
||
|
|
" | \n",
|
||
|
|
" | predict_log_proba(self, X)\n",
|
||
|
|
" | Predict class log-probabilities for X.\n",
|
||
|
|
" | \n",
|
||
|
|
" | The predicted class log-probabilities of an input sample is computed as\n",
|
||
|
|
" | the log of the mean predicted class probabilities of the trees in the\n",
|
||
|
|
" | forest.\n",
|
||
|
|
" | \n",
|
||
|
|
" | Parameters\n",
|
||
|
|
" | ----------\n",
|
||
|
|
" | X : {array-like, sparse matrix} of shape (n_samples, n_features)\n",
|
||
|
|
" | The input samples. Internally, its dtype will be converted to\n",
|
||
|
|
" | ``dtype=np.float32``. If a sparse matrix is provided, it will be\n",
|
||
|
|
" | converted into a sparse ``csr_matrix``.\n",
|
||
|
|
" | \n",
|
||
|
|
" | Returns\n",
|
||
|
|
" | -------\n",
|
||
|
|
" | p : ndarray of shape (n_samples, n_classes), or a list of n_outputs\n",
|
||
|
|
" | such arrays if n_outputs > 1.\n",
|
||
|
|
" | The class probabilities of the input samples. The order of the\n",
|
||
|
|
" | classes corresponds to that in the attribute :term:`classes_`.\n",
|
||
|
|
" | \n",
|
||
|
|
" | predict_proba(self, X)\n",
|
||
|
|
" | Predict class probabilities for X.\n",
|
||
|
|
" | \n",
|
||
|
|
" | The predicted class probabilities of an input sample are computed as\n",
|
||
|
|
" | the mean predicted class probabilities of the trees in the forest.\n",
|
||
|
|
" | The class probability of a single tree is the fraction of samples of\n",
|
||
|
|
" | the same class in a leaf.\n",
|
||
|
|
" | \n",
|
||
|
|
" | Parameters\n",
|
||
|
|
" | ----------\n",
|
||
|
|
" | X : {array-like, sparse matrix} of shape (n_samples, n_features)\n",
|
||
|
|
" | The input samples. Internally, its dtype will be converted to\n",
|
||
|
|
" | ``dtype=np.float32``. If a sparse matrix is provided, it will be\n",
|
||
|
|
" | converted into a sparse ``csr_matrix``.\n",
|
||
|
|
" | \n",
|
||
|
|
" | Returns\n",
|
||
|
|
" | -------\n",
|
||
|
|
" | p : ndarray of shape (n_samples, n_classes), or a list of n_outputs\n",
|
||
|
|
" | such arrays if n_outputs > 1.\n",
|
||
|
|
" | The class probabilities of the input samples. The order of the\n",
|
||
|
|
" | classes corresponds to that in the attribute :term:`classes_`.\n",
|
||
|
|
" | \n",
|
||
|
|
" | ----------------------------------------------------------------------\n",
|
||
|
|
" | Methods inherited from sklearn.base.ClassifierMixin:\n",
|
||
|
|
" | \n",
|
||
|
|
" | score(self, X, y, sample_weight=None)\n",
|
||
|
|
" | Return the mean accuracy on the given test data and labels.\n",
|
||
|
|
" | \n",
|
||
|
|
" | In multi-label classification, this is the subset accuracy\n",
|
||
|
|
" | which is a harsh metric since you require for each sample that\n",
|
||
|
|
" | each label set be correctly predicted.\n",
|
||
|
|
" | \n",
|
||
|
|
" | Parameters\n",
|
||
|
|
" | ----------\n",
|
||
|
|
" | X : array-like of shape (n_samples, n_features)\n",
|
||
|
|
" | Test samples.\n",
|
||
|
|
" | \n",
|
||
|
|
" | y : array-like of shape (n_samples,) or (n_samples, n_outputs)\n",
|
||
|
|
" | True labels for X.\n",
|
||
|
|
" | \n",
|
||
|
|
" | sample_weight : array-like of shape (n_samples,), default=None\n",
|
||
|
|
" | Sample weights.\n",
|
||
|
|
" | \n",
|
||
|
|
" | Returns\n",
|
||
|
|
" | -------\n",
|
||
|
|
" | score : float\n",
|
||
|
|
" | Mean accuracy of self.predict(X) wrt. y.\n",
|
||
|
|
" | \n",
|
||
|
|
" | ----------------------------------------------------------------------\n",
|
||
|
|
" | Data descriptors inherited from sklearn.base.ClassifierMixin:\n",
|
||
|
|
" | \n",
|
||
|
|
" | __dict__\n",
|
||
|
|
" | dictionary for instance variables (if defined)\n",
|
||
|
|
" | \n",
|
||
|
|
" | __weakref__\n",
|
||
|
|
" | list of weak references to the object (if defined)\n",
|
||
|
|
" | \n",
|
||
|
|
" | ----------------------------------------------------------------------\n",
|
||
|
|
" | Methods inherited from BaseForest:\n",
|
||
|
|
" | \n",
|
||
|
|
" | apply(self, X)\n",
|
||
|
|
" | Apply trees in the forest to X, return leaf indices.\n",
|
||
|
|
" | \n",
|
||
|
|
" | Parameters\n",
|
||
|
|
" | ----------\n",
|
||
|
|
" | X : {array-like, sparse matrix} of shape (n_samples, n_features)\n",
|
||
|
|
" | The input samples. Internally, its dtype will be converted to\n",
|
||
|
|
" | ``dtype=np.float32``. If a sparse matrix is provided, it will be\n",
|
||
|
|
" | converted into a sparse ``csr_matrix``.\n",
|
||
|
|
" | \n",
|
||
|
|
" | Returns\n",
|
||
|
|
" | -------\n",
|
||
|
|
" | X_leaves : ndarray of shape (n_samples, n_estimators)\n",
|
||
|
|
" | For each datapoint x in X and for each tree in the forest,\n",
|
||
|
|
" | return the index of the leaf x ends up in.\n",
|
||
|
|
" | \n",
|
||
|
|
" | decision_path(self, X)\n",
|
||
|
|
" | Return the decision path in the forest.\n",
|
||
|
|
" | \n",
|
||
|
|
" | .. versionadded:: 0.18\n",
|
||
|
|
" | \n",
|
||
|
|
" | Parameters\n",
|
||
|
|
" | ----------\n",
|
||
|
|
" | X : {array-like, sparse matrix} of shape (n_samples, n_features)\n",
|
||
|
|
" | The input samples. Internally, its dtype will be converted to\n",
|
||
|
|
" | ``dtype=np.float32``. If a sparse matrix is provided, it will be\n",
|
||
|
|
" | converted into a sparse ``csr_matrix``.\n",
|
||
|
|
" | \n",
|
||
|
|
" | Returns\n",
|
||
|
|
" | -------\n",
|
||
|
|
" | indicator : sparse matrix of shape (n_samples, n_nodes)\n",
|
||
|
|
" | Return a node indicator matrix where non zero elements indicates\n",
|
||
|
|
" | that the samples goes through the nodes. The matrix is of CSR\n",
|
||
|
|
" | format.\n",
|
||
|
|
" | \n",
|
||
|
|
" | n_nodes_ptr : ndarray of shape (n_estimators + 1,)\n",
|
||
|
|
" | The columns from indicator[n_nodes_ptr[i]:n_nodes_ptr[i+1]]\n",
|
||
|
|
" | gives the indicator value for the i-th estimator.\n",
|
||
|
|
" | \n",
|
||
|
|
" | fit(self, X, y, sample_weight=None)\n",
|
||
|
|
" | Build a forest of trees from the training set (X, y).\n",
|
||
|
|
" | \n",
|
||
|
|
" | Parameters\n",
|
||
|
|
" | ----------\n",
|
||
|
|
" | X : {array-like, sparse matrix} of shape (n_samples, n_features)\n",
|
||
|
|
" | The training input samples. Internally, its dtype will be converted\n",
|
||
|
|
" | to ``dtype=np.float32``. If a sparse matrix is provided, it will be\n",
|
||
|
|
" | converted into a sparse ``csc_matrix``.\n",
|
||
|
|
" | \n",
|
||
|
|
" | y : array-like of shape (n_samples,) or (n_samples, n_outputs)\n",
|
||
|
|
" | The target values (class labels in classification, real numbers in\n",
|
||
|
|
" | regression).\n",
|
||
|
|
" | \n",
|
||
|
|
" | sample_weight : array-like of shape (n_samples,), default=None\n",
|
||
|
|
" | Sample weights. If None, then samples are equally weighted. Splits\n",
|
||
|
|
" | that would create child nodes with net zero or negative weight are\n",
|
||
|
|
" | ignored while searching for a split in each node. In the case of\n",
|
||
|
|
" | classification, splits are also ignored if they would result in any\n",
|
||
|
|
" | single class carrying a negative weight in either child node.\n",
|
||
|
|
" | \n",
|
||
|
|
" | Returns\n",
|
||
|
|
" | -------\n",
|
||
|
|
" | self : object\n",
|
||
|
|
" | \n",
|
||
|
|
" | ----------------------------------------------------------------------\n",
|
||
|
|
" | Readonly properties inherited from BaseForest:\n",
|
||
|
|
" | \n",
|
||
|
|
" | feature_importances_\n",
|
||
|
|
" | The impurity-based feature importances.\n",
|
||
|
|
" | \n",
|
||
|
|
" | The higher, the more important the feature.\n",
|
||
|
|
" | The importance of a feature is computed as the (normalized)\n",
|
||
|
|
" | total reduction of the criterion brought by that feature. It is also\n",
|
||
|
|
" | known as the Gini importance.\n",
|
||
|
|
" | \n",
|
||
|
|
" | Warning: impurity-based feature importances can be misleading for\n",
|
||
|
|
" | high cardinality features (many unique values). See\n",
|
||
|
|
" | :func:`sklearn.inspection.permutation_importance` as an alternative.\n",
|
||
|
|
" | \n",
|
||
|
|
" | Returns\n",
|
||
|
|
" | -------\n",
|
||
|
|
" | feature_importances_ : ndarray of shape (n_features,)\n",
|
||
|
|
" | The values of this array sum to 1, unless all trees are single node\n",
|
||
|
|
" | trees consisting of only the root node, in which case it will be an\n",
|
||
|
|
" | array of zeros.\n",
|
||
|
|
" | \n",
|
||
|
|
" | ----------------------------------------------------------------------\n",
|
||
|
|
" | Methods inherited from sklearn.ensemble._base.BaseEnsemble:\n",
|
||
|
|
" | \n",
|
||
|
|
" | __getitem__(self, index)\n",
|
||
|
|
" | Return the index'th estimator in the ensemble.\n",
|
||
|
|
" | \n",
|
||
|
|
" | __iter__(self)\n",
|
||
|
|
" | Return iterator over estimators in the ensemble.\n",
|
||
|
|
" | \n",
|
||
|
|
" | __len__(self)\n",
|
||
|
|
" | Return the number of estimators in the ensemble.\n",
|
||
|
|
" | \n",
|
||
|
|
" | ----------------------------------------------------------------------\n",
|
||
|
|
" | Data and other attributes inherited from sklearn.ensemble._base.BaseEnsemble:\n",
|
||
|
|
" | \n",
|
||
|
|
" | __annotations__ = {'_required_parameters': typing.List[str]}\n",
|
||
|
|
" | \n",
|
||
|
|
" | ----------------------------------------------------------------------\n",
|
||
|
|
" | Methods inherited from sklearn.base.BaseEstimator:\n",
|
||
|
|
" | \n",
|
||
|
|
" | __getstate__(self)\n",
|
||
|
|
" | \n",
|
||
|
|
" | __repr__(self, N_CHAR_MAX=700)\n",
|
||
|
|
" | Return repr(self).\n",
|
||
|
|
" | \n",
|
||
|
|
" | __setstate__(self, state)\n",
|
||
|
|
" | \n",
|
||
|
|
" | get_params(self, deep=True)\n",
|
||
|
|
" | Get parameters for this estimator.\n",
|
||
|
|
" | \n",
|
||
|
|
" | Parameters\n",
|
||
|
|
" | ----------\n",
|
||
|
|
" | deep : bool, default=True\n",
|
||
|
|
" | If True, will return the parameters for this estimator and\n",
|
||
|
|
" | contained subobjects that are estimators.\n",
|
||
|
|
" | \n",
|
||
|
|
" | Returns\n",
|
||
|
|
" | -------\n",
|
||
|
|
" | params : mapping of string to any\n",
|
||
|
|
" | Parameter names mapped to their values.\n",
|
||
|
|
" | \n",
|
||
|
|
" | set_params(self, **params)\n",
|
||
|
|
" | Set the parameters of this estimator.\n",
|
||
|
|
" | \n",
|
||
|
|
" | The method works on simple estimators as well as on nested objects\n",
|
||
|
|
" | (such as pipelines). The latter have parameters of the form\n",
|
||
|
|
" | ``<component>__<parameter>`` so that it's possible to update each\n",
|
||
|
|
" | component of a nested object.\n",
|
||
|
|
" | \n",
|
||
|
|
" | Parameters\n",
|
||
|
|
" | ----------\n",
|
||
|
|
" | **params : dict\n",
|
||
|
|
" | Estimator parameters.\n",
|
||
|
|
" | \n",
|
||
|
|
" | Returns\n",
|
||
|
|
" | -------\n",
|
||
|
|
" | self : object\n",
|
||
|
|
" | Estimator instance.\n",
|
||
|
|
"\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"help(RandomForestClassifier)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 98,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"# Use 10 random trees\n",
|
||
|
|
"model = RandomForestClassifier(n_estimators=10,max_features='auto',random_state=101)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 99,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"RandomForestClassifier(n_estimators=10, random_state=101)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 99,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"model.fit(X_train,y_train)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 100,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"preds = model.predict(X_test)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"## Evaluation"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 101,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"from sklearn.metrics import confusion_matrix,classification_report,plot_confusion_matrix,accuracy_score"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 102,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"array([[39, 2, 0],\n",
|
||
|
|
" [ 1, 22, 0],\n",
|
||
|
|
" [ 0, 0, 37]], dtype=int64)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 102,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"confusion_matrix(y_test,preds)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 103,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x1384588b0d0>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 103,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 432x288 with 2 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"plot_confusion_matrix(model,X_test,y_test)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"## Feature Importance\n",
|
||
|
|
"\n",
|
||
|
|
"Very useful attribute of the trained model!"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 52,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"array([0.35324545, 0.13320651, 0.1985798 , 0.12074795, 0.14244127,\n",
|
||
|
|
" 0.03781403, 0.00677831, 0.00718669])"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 52,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"model.feature_importances_"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"## Choosing correct number of trees"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"Let's explore if continually adding more trees improves performance..."
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 53,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"test_error = []\n",
|
||
|
|
"\n",
|
||
|
|
"for n in range(1,40):\n",
|
||
|
|
" # Use n random trees\n",
|
||
|
|
" model = RandomForestClassifier(n_estimators=n,max_features='auto')\n",
|
||
|
|
" model.fit(X_train,y_train)\n",
|
||
|
|
" test_preds = model.predict(X_test)\n",
|
||
|
|
" test_error.append(1-accuracy_score(test_preds,y_test))\n",
|
||
|
|
" "
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 54,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.legend.Legend at 0x138491ef760>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 54,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAD4CAYAAADiry33AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAA2l0lEQVR4nO29e5Bb93Xn+Tl4NPoJgGx2N0lAFEmJFEUSHdqm5UliWYm1kuXMWpIn8ki2M9FkNON4vXJ2y+W15XWtR3GNK9HMJtrJ2hXHiTVylMxIXmVcpmOlNJmRNbZHjixKlhok9aKoB9F8Nwn0uxto/PYP3AuCaDwugIsGCJxPVVcDF7/7u+deAN/7w/n9zjlijEFRFEXpXDytNkBRFEVpLir0iqIoHY4KvaIoSoejQq8oitLhqNAriqJ0OL5WG1DMhg0bzNatW1tthqIoymXF888/f84YM1LqtbYT+q1bt3Lw4MFWm6EoinJZISJvl3tNXTeKoigdjgq9oihKh6NCryiK0uE48tGLyC3Avwe8wF8YY/6w6PUA8JfAe4Ap4E5jzFvWa+PAnwFBIAu81xiz6NYJKIpyeZBOp0kkEiwu6te/EXp7e4lGo/j9fsf7VBV6EfEC3wBuAhLAcyJywBhzpKDZPcAFY8zVInIX8ABwp4j4gL8C/pkx5iURGQbSzk9JUZROIZFIMDQ0xNatWxGRVptzWWKMYWpqikQiwbZt2xzv58R1cx1w1BhzzBizDDwK3FbU5jbgO9bjx4EbJfdO3gxMGGNesoycMsasOLZOUZSOYXFxkeHhYRX5BhARhoeHa/5V5EToI8DxgucJa1vJNsaYDJAChoGdgBGRJ0XkBRH5QhnjPyUiB0Xk4NmzZ2s6AUVRLh9U5BunnmvY7MlYH/B+4JPW/4+KyI3FjYwx3zLG7DfG7B8ZKbnevyqJC/P830++yvHz8w0ZrCiK0mk4EfpJ4IqC51FrW8k2ll8+RG5SNgH82BhzzhgzDzwBvLtRo0sxt7TC1390lINvn29G94qiXOZMTU2xb98+9u3bx8aNG4lEIvnny8vLVfd/+umneeaZZ0q+9vDDDzMyMpLvb9++fRw5cqRk21bgZNXNc8AOEdlGTtDvAj5R1OYAcDfwM+AO4CljjBGRJ4EviEg/sAzcADzolvGFXDUyQJ/fy0QixUffFW3GIRRFuYwZHh7mxRdfBOD+++9ncHCQz3/+8473f/rppxkcHORXfuVXSr5+55138vWvf73s/plMBp/PV/a50/3qoeqI3vK53ws8CbwMfNcYc1hEvioit1rNvg0Mi8hR4HPAfda+F4A/JnezeBF4wRjzw4YsLoPP62HP5iCHJlPN6F5RlA7k+eef54YbbuA973kPH/rQhzh58iQAf/Inf8Lu3bsZHx/nrrvu4q233uKb3/wmDz74IPv27eMnP/mJo/6ffvpprr/+em699VZ279696vni4iK/8zu/QywW413vehc/+tGPgNwvhFtvvZUPfvCD3HjjKm93zTi6TRhjniDndinc9pWCx4vAx8rs+1fkllg2nVg0xKM/P85K1uD16KSPorQrv/+Dwxw5Me1qn7s3B/nXH9njuL0xhs9+9rN8//vfZ2RkhMcee4wvf/nLPPTQQ/zhH/4hb775JoFAgGQySTgc5tOf/nTFXwGPPfYYP/3pT/PPf/aznwHwwgsvcOjQIbZt28bTTz99yfM/+qM/QkSIx+O88sor3Hzzzbz22mv5/SYmJli/fn0DVyVH2yU1a4TxaIj/8D/e4o2zs+wcG2q1OYqitDFLS0scOnSIm266CYCVlRU2bdoEwPj4OJ/85Ce5/fbbuf322x31V851c911112y5r3w+U9/+lM++9nPArBr1y6uvPLKvNDfdNNNrog8dJjQxyIhACYSKRV6RWljahl5NwtjDHv27MmPvAv54Q9/yI9//GN+8IMf8LWvfY14PF73cQYGBio+d7pfI3RUrpttGwYZ6PESTyRbbYqiKG1OIBDg7NmzeaFPp9McPnyYbDbL8ePH+fVf/3UeeOABUqkUs7OzDA0NMTMz46oN119/PX/9138NwGuvvcY777zDNddc4+oxoMOE3usR9kRCTOiErKIoVfB4PDz++ON88Ytf5Jd+6ZfYt28fzzzzDCsrK/zWb/1WfoL0937v9wiHw3zkIx/he9/7XtnJ2Mcee+yS5ZXllmIW8pnPfIZsNkssFuPOO+/k4YcfJhAIuH6uYoxxvdNG2L9/v2mk8Mi/+dsjPPIPb3Po9z+E39tR9zFFuax5+eWXufbaa1ttRkdQ6lqKyPPGmP2l2necEsaiIZYyWV4/PdtqUxRFUdqCjhP68WgYQNfTK4qiWHSc0F+5vp+hXh8Tk8lWm6IoShHt5iq+HKnnGnac0Hs8wt7NIeIJHdErSjvR29vL1NSUin0D2Pnoe3t7a9qvo9bR29iBU8uZLD2+jruXKcplSTQaJZFIoKnIG8OuMFULHSn0sWiI5ZUsr52eYa8VRKUoSmvx+/01VUVS3KMjh7vjkTCQi5BVFEXpdjpS6K9Y30eoz09cJ2QVRVE6U+hFhPFoSEf0iqIodKjQA+yNhHjt9AyLaa1FrihKd9OxQj8eCZFeMbx6yt0kRIqiKJcbHSv0saiVslgjZBVF6XI6Vugj4T7WD/RoymJFUbqejhV6ESEW0QlZRVGUjhV6yEXIvn5mloVlnZBVFKV76Wihj0VCrGQNR066W4RYURTlcqKzhd6akFU/vaIo3UxHC/3GYC8bBgPEJ3VEryhK99LRQm9HyGoqBEVRupmOFnrI+emPnpllbinTalMURVFaQscL/Xg0RNagE7KKonQtHS/0MSsfva6nVxSlW+l4oR8N9rIx2KsrbxRF6Vo6Xughl8lSc94oitKtdIXQj0dDHDs7x8xiutWmKIqirDldIfR24NThEzohqyhK9+FI6EXkFhF5VUSOish9JV4PiMhj1uvPishWa/tWEVkQkRetv2+6bL8j7AnZuE7IKorShfiqNRARL/AN4CYgATwnIgeMMUcKmt0DXDDGXC0idwEPAHdar71hjNnnrtm1sWEwQCTcp356RVG6Eicj+uuAo8aYY8aYZeBR4LaiNrcB37EePw7cKCLinpmNE4uEdOWNoihdiROhjwDHC54nrG0l2xhjMkAKGLZe2yYivxCR/y4i15c6gIh8SkQOisjBs2fP1nQCTolFQ7w1NU9qXidkFUXpLpo9GXsS2GKMeRfwOeA/ikiwuJEx5lvGmP3GmP0jIyNNMSTvp1f3jaIoXYYToZ8Erih4HrW2lWwjIj4gBEwZY5aMMVMAxpjngTeAnY0aXQ8q9IqidCtOhP45YIeIbBORHuAu4EBRmwPA3dbjO4CnjDFGREasyVxEZDuwAzjmjum1sW6ghyvW92kmS0VRuo6qq26MMRkRuRd4EvACDxljDovIV4GDxpgDwLeBR0TkKHCe3M0A4APAV0UkDWSBTxtjzjfjRJwwHgkzoUKvKEqXUVXoAYwxTwBPFG37SsHjReBjJfb7G+BvGrTRNWLRED+Mn+TC3DLrBnpabY6iKMqa0BWRsTbj6qdXFKUL6Sqh36NCryhKF9JVQh/q87N1uJ8JDZxSFKWL6CqhB4hFw5rzRlGUrqLrhH48EuJEapFzs0utNkVRFGVN6Dqht1MWq59eUZRuoeuEfs/mICKaslhRlO6h64R+qNfP9g0DWixcUZSuoeuEHmA8GtZUCIqidA1dKfSxSIjT00ucnl5stSmKoihNpzuFPqqlBRVF6R66Uuh3bwriEbS0oKIoXUFXCv1AwMfVo4McUqFXFKUL6EqhB4hFwkwkUhhjWm2KoihKU+laoR+Phjg3u8QpnZBVFKXD6VqhtydkdT29oiidTtcK/e5NQbwe0ZU3iqJ0PF0r9L1+LzvHhnTljaIoHU/XCj1ALBLk0KROyCqK0tl0t9BHw5yfW2YyudBqUxRFUZpGVwt9voas+ukVRelgulrod20awu8V9dMritLRdLXQB3xertk4pCN6RVE6mq4WerAjZJM6IasoSsfS9UI/Hg0xvZjhnfPzrTZFURSlKXS90MciWkN
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 432x288 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"plt.plot(range(1,40),test_error,label='Test Error')\n",
|
||
|
|
"plt.legend()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"Clearly there are diminishing returns, on such a small dataset, we've pretty much extracted all the information we can after about 5 trees."
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"# Random Forest - HyperParameter Exploration"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"https://archive.ics.uci.edu/ml/datasets/banknote+authentication"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 55,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"df = pd.read_csv(\"../DATA/data_banknote_authentication.csv\")"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 56,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/html": [
|
||
|
|
"<div>\n",
|
||
|
|
"<style scoped>\n",
|
||
|
|
" .dataframe tbody tr th:only-of-type {\n",
|
||
|
|
" vertical-align: middle;\n",
|
||
|
|
" }\n",
|
||
|
|
"\n",
|
||
|
|
" .dataframe tbody tr th {\n",
|
||
|
|
" vertical-align: top;\n",
|
||
|
|
" }\n",
|
||
|
|
"\n",
|
||
|
|
" .dataframe thead th {\n",
|
||
|
|
" text-align: right;\n",
|
||
|
|
" }\n",
|
||
|
|
"</style>\n",
|
||
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
||
|
|
" <thead>\n",
|
||
|
|
" <tr style=\"text-align: right;\">\n",
|
||
|
|
" <th></th>\n",
|
||
|
|
" <th>Variance_Wavelet</th>\n",
|
||
|
|
" <th>Skewness_Wavelet</th>\n",
|
||
|
|
" <th>Curtosis_Wavelet</th>\n",
|
||
|
|
" <th>Image_Entropy</th>\n",
|
||
|
|
" <th>Class</th>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" </thead>\n",
|
||
|
|
" <tbody>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>0</th>\n",
|
||
|
|
" <td>3.62160</td>\n",
|
||
|
|
" <td>8.6661</td>\n",
|
||
|
|
" <td>-2.8073</td>\n",
|
||
|
|
" <td>-0.44699</td>\n",
|
||
|
|
" <td>0</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>1</th>\n",
|
||
|
|
" <td>4.54590</td>\n",
|
||
|
|
" <td>8.1674</td>\n",
|
||
|
|
" <td>-2.4586</td>\n",
|
||
|
|
" <td>-1.46210</td>\n",
|
||
|
|
" <td>0</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>2</th>\n",
|
||
|
|
" <td>3.86600</td>\n",
|
||
|
|
" <td>-2.6383</td>\n",
|
||
|
|
" <td>1.9242</td>\n",
|
||
|
|
" <td>0.10645</td>\n",
|
||
|
|
" <td>0</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>3</th>\n",
|
||
|
|
" <td>3.45660</td>\n",
|
||
|
|
" <td>9.5228</td>\n",
|
||
|
|
" <td>-4.0112</td>\n",
|
||
|
|
" <td>-3.59440</td>\n",
|
||
|
|
" <td>0</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>4</th>\n",
|
||
|
|
" <td>0.32924</td>\n",
|
||
|
|
" <td>-4.4552</td>\n",
|
||
|
|
" <td>4.5718</td>\n",
|
||
|
|
" <td>-0.98880</td>\n",
|
||
|
|
" <td>0</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" </tbody>\n",
|
||
|
|
"</table>\n",
|
||
|
|
"</div>"
|
||
|
|
],
|
||
|
|
"text/plain": [
|
||
|
|
" Variance_Wavelet Skewness_Wavelet Curtosis_Wavelet Image_Entropy Class\n",
|
||
|
|
"0 3.62160 8.6661 -2.8073 -0.44699 0\n",
|
||
|
|
"1 4.54590 8.1674 -2.4586 -1.46210 0\n",
|
||
|
|
"2 3.86600 -2.6383 1.9242 0.10645 0\n",
|
||
|
|
"3 3.45660 9.5228 -4.0112 -3.59440 0\n",
|
||
|
|
"4 0.32924 -4.4552 4.5718 -0.98880 0"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 56,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"df.head()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 57,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<seaborn.axisgrid.PairGrid at 0x13849319fa0>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 57,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 762.375x720 with 20 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"sns.pairplot(df,hue='Class')"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 58,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"X = df.drop(\"Class\",axis=1)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 59,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"y = df[\"Class\"]"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 60,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"from sklearn.model_selection import train_test_split"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 61,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.15, random_state=101)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 62,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"from sklearn.model_selection import GridSearchCV"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 63,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"n_estimators=[64,100,128,200]\n",
|
||
|
|
"max_features= [2,3,4]\n",
|
||
|
|
"bootstrap = [True,False]"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 64,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"param_grid = {'n_estimators':n_estimators,\n",
|
||
|
|
" 'max_features':max_features,\n",
|
||
|
|
" 'bootstrap':bootstrap}"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 65,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"rfc = RandomForestClassifier(oob_score=True) # Note, oob_score only makes sense when bootstrap=True!\n",
|
||
|
|
"grid = GridSearchCV(rfc,param_grid)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 66,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stderr",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:548: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
|
||
|
|
"Traceback (most recent call last):\n",
|
||
|
|
" File \"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 531, in _fit_and_score\n",
|
||
|
|
" estimator.fit(X_train, y_train, **fit_params)\n",
|
||
|
|
" File \"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\ensemble\\_forest.py\", line 351, in fit\n",
|
||
|
|
" raise ValueError(\"Out of bag estimation only available\"\n",
|
||
|
|
"ValueError: Out of bag estimation only available if bootstrap=True\n",
|
||
|
|
"\n",
|
||
|
|
" warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
|
||
|
|
"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:548: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
|
||
|
|
"Traceback (most recent call last):\n",
|
||
|
|
" File \"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 531, in _fit_and_score\n",
|
||
|
|
" estimator.fit(X_train, y_train, **fit_params)\n",
|
||
|
|
" File \"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\ensemble\\_forest.py\", line 351, in fit\n",
|
||
|
|
" raise ValueError(\"Out of bag estimation only available\"\n",
|
||
|
|
"ValueError: Out of bag estimation only available if bootstrap=True\n",
|
||
|
|
"\n",
|
||
|
|
" warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
|
||
|
|
"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:548: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
|
||
|
|
"Traceback (most recent call last):\n",
|
||
|
|
" File \"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 531, in _fit_and_score\n",
|
||
|
|
" estimator.fit(X_train, y_train, **fit_params)\n",
|
||
|
|
" File \"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\ensemble\\_forest.py\", line 351, in fit\n",
|
||
|
|
" raise ValueError(\"Out of bag estimation only available\"\n",
|
||
|
|
"ValueError: Out of bag estimation only available if bootstrap=True\n",
|
||
|
|
"\n",
|
||
|
|
" warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
|
||
|
|
"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:548: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
|
||
|
|
"Traceback (most recent call last):\n",
|
||
|
|
" File \"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 531, in _fit_and_score\n",
|
||
|
|
" estimator.fit(X_train, y_train, **fit_params)\n",
|
||
|
|
" File \"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\ensemble\\_forest.py\", line 351, in fit\n",
|
||
|
|
" raise ValueError(\"Out of bag estimation only available\"\n",
|
||
|
|
"ValueError: Out of bag estimation only available if bootstrap=True\n",
|
||
|
|
"\n",
|
||
|
|
" warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
|
||
|
|
"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:548: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
|
||
|
|
"Traceback (most recent call last):\n",
|
||
|
|
" File \"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 531, in _fit_and_score\n",
|
||
|
|
" estimator.fit(X_train, y_train, **fit_params)\n",
|
||
|
|
" File \"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\ensemble\\_forest.py\", line 351, in fit\n",
|
||
|
|
" raise ValueError(\"Out of bag estimation only available\"\n",
|
||
|
|
"ValueError: Out of bag estimation only available if bootstrap=True\n",
|
||
|
|
"\n",
|
||
|
|
" warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
|
||
|
|
"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:548: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
|
||
|
|
"Traceback (most recent call last):\n",
|
||
|
|
" File \"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 531, in _fit_and_score\n",
|
||
|
|
" estimator.fit(X_train, y_train, **fit_params)\n",
|
||
|
|
" File \"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\ensemble\\_forest.py\", line 351, in fit\n",
|
||
|
|
" raise ValueError(\"Out of bag estimation only available\"\n",
|
||
|
|
"ValueError: Out of bag estimation only available if bootstrap=True\n",
|
||
|
|
"\n",
|
||
|
|
" warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
|
||
|
|
"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:548: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
|
||
|
|
"Traceback (most recent call last):\n",
|
||
|
|
" File \"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 531, in _fit_and_score\n",
|
||
|
|
" estimator.fit(X_train, y_train, **fit_params)\n",
|
||
|
|
" File \"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\ensemble\\_forest.py\", line 351, in fit\n",
|
||
|
|
" raise ValueError(\"Out of bag estimation only available\"\n",
|
||
|
|
"ValueError: Out of bag estimation only available if bootstrap=True\n",
|
||
|
|
"\n",
|
||
|
|
" warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
|
||
|
|
"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:548: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
|
||
|
|
"Traceback (most recent call last):\n",
|
||
|
|
" File \"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 531, in _fit_and_score\n",
|
||
|
|
" estimator.fit(X_train, y_train, **fit_params)\n",
|
||
|
|
" File \"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\ensemble\\_forest.py\", line 351, in fit\n",
|
||
|
|
" raise ValueError(\"Out of bag estimation only available\"\n",
|
||
|
|
"ValueError: Out of bag estimation only available if bootstrap=True\n",
|
||
|
|
"\n",
|
||
|
|
" warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
|
||
|
|
"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:548: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
|
||
|
|
"Traceback (most recent call last):\n",
|
||
|
|
" File \"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 531, in _fit_and_score\n",
|
||
|
|
" estimator.fit(X_train, y_train, **fit_params)\n",
|
||
|
|
" File \"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\ensemble\\_forest.py\", line 351, in fit\n",
|
||
|
|
" raise ValueError(\"Out of bag estimation only available\"\n",
|
||
|
|
"ValueError: Out of bag estimation only available if bootstrap=True\n",
|
||
|
|
"\n",
|
||
|
|
" warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
|
||
|
|
"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:548: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
|
||
|
|
"Traceback (most recent call last):\n",
|
||
|
|
" File \"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 531, in _fit_and_score\n",
|
||
|
|
" estimator.fit(X_train, y_train, **fit_params)\n",
|
||
|
|
" File \"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\ensemble\\_forest.py\", line 351, in fit\n",
|
||
|
|
" raise ValueError(\"Out of bag estimation only available\"\n",
|
||
|
|
"ValueError: Out of bag estimation only available if bootstrap=True\n",
|
||
|
|
"\n",
|
||
|
|
" warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
|
||
|
|
"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:548: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
|
||
|
|
"Traceback (most recent call last):\n",
|
||
|
|
" File \"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 531, in _fit_and_score\n",
|
||
|
|
" estimator.fit(X_train, y_train, **fit_params)\n",
|
||
|
|
" File \"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\ensemble\\_forest.py\", line 351, in fit\n",
|
||
|
|
" raise ValueError(\"Out of bag estimation only available\"\n",
|
||
|
|
"ValueError: Out of bag estimation only available if bootstrap=True\n",
|
||
|
|
"\n",
|
||
|
|
" warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
|
||
|
|
"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:548: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
|
||
|
|
"Traceback (most recent call last):\n",
|
||
|
|
" File \"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 531, in _fit_and_score\n",
|
||
|
|
" estimator.fit(X_train, y_train, **fit_params)\n",
|
||
|
|
" File \"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\ensemble\\_forest.py\", line 351, in fit\n",
|
||
|
|
" raise ValueError(\"Out of bag estimation only available\"\n",
|
||
|
|
"ValueError: Out of bag estimation only available if bootstrap=True\n",
|
||
|
|
"\n",
|
||
|
|
" warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
|
||
|
|
"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:548: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
|
||
|
|
"Traceback (most recent call last):\n",
|
||
|
|
" File \"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 531, in _fit_and_score\n",
|
||
|
|
" estimator.fit(X_train, y_train, **fit_params)\n",
|
||
|
|
" File \"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\ensemble\\_forest.py\", line 351, in fit\n",
|
||
|
|
" raise ValueError(\"Out of bag estimation only available\"\n",
|
||
|
|
"ValueError: Out of bag estimation only available if bootstrap=True\n",
|
||
|
|
"\n",
|
||
|
|
" warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
|
||
|
|
"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:548: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
|
||
|
|
"Traceback (most recent call last):\n",
|
||
|
|
" File \"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 531, in _fit_and_score\n",
|
||
|
|
" estimator.fit(X_train, y_train, **fit_params)\n",
|
||
|
|
" File \"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\ensemble\\_forest.py\", line 351, in fit\n",
|
||
|
|
" raise ValueError(\"Out of bag estimation only available\"\n",
|
||
|
|
"ValueError: Out of bag estimation only available if bootstrap=True\n",
|
||
|
|
"\n",
|
||
|
|
" warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
|
||
|
|
"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:548: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
|
||
|
|
"Traceback (most recent call last):\n",
|
||
|
|
" File \"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 531, in _fit_and_score\n",
|
||
|
|
" estimator.fit(X_train, y_train, **fit_params)\n",
|
||
|
|
" File \"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\ensemble\\_forest.py\", line 351, in fit\n",
|
||
|
|
" raise ValueError(\"Out of bag estimation only available\"\n",
|
||
|
|
"ValueError: Out of bag estimation only available if bootstrap=True\n",
|
||
|
|
"\n",
|
||
|
|
" warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
|
||
|
|
"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:548: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
|
||
|
|
"Traceback (most recent call last):\n",
|
||
|
|
" File \"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 531, in _fit_and_score\n",
|
||
|
|
" estimator.fit(X_train, y_train, **fit_params)\n",
|
||
|
|
" File \"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\ensemble\\_forest.py\", line 351, in fit\n",
|
||
|
|
" raise ValueError(\"Out of bag estimation only available\"\n",
|
||
|
|
"ValueError: Out of bag estimation only available if bootstrap=True\n",
|
||
|
|
"\n",
|
||
|
|
" warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
|
||
|
|
"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:548: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
|
||
|
|
"Traceback (most recent call last):\n",
|
||
|
|
" File \"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 531, in _fit_and_score\n",
|
||
|
|
" estimator.fit(X_train, y_train, **fit_params)\n",
|
||
|
|
" File \"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\ensemble\\_forest.py\", line 351, in fit\n",
|
||
|
|
" raise ValueError(\"Out of bag estimation only available\"\n",
|
||
|
|
"ValueError: Out of bag estimation only available if bootstrap=True\n",
|
||
|
|
"\n",
|
||
|
|
" warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
|
||
|
|
"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:548: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
|
||
|
|
"Traceback (most recent call last):\n",
|
||
|
|
" File \"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 531, in _fit_and_score\n",
|
||
|
|
" estimator.fit(X_train, y_train, **fit_params)\n",
|
||
|
|
" File \"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\ensemble\\_forest.py\", line 351, in fit\n",
|
||
|
|
" raise ValueError(\"Out of bag estimation only available\"\n",
|
||
|
|
"ValueError: Out of bag estimation only available if bootstrap=True\n",
|
||
|
|
"\n",
|
||
|
|
" warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
|
||
|
|
"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:548: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
|
||
|
|
"Traceback (most recent call last):\n",
|
||
|
|
" File \"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 531, in _fit_and_score\n",
|
||
|
|
" estimator.fit(X_train, y_train, **fit_params)\n",
|
||
|
|
" File \"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\ensemble\\_forest.py\", line 351, in fit\n",
|
||
|
|
" raise ValueError(\"Out of bag estimation only available\"\n",
|
||
|
|
"ValueError: Out of bag estimation only available if bootstrap=True\n",
|
||
|
|
"\n",
|
||
|
|
" warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
|
||
|
|
"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:548: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
|
||
|
|
"Traceback (most recent call last):\n",
|
||
|
|
" File \"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 531, in _fit_and_score\n",
|
||
|
|
" estimator.fit(X_train, y_train, **fit_params)\n",
|
||
|
|
" File \"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\ensemble\\_forest.py\", line 351, in fit\n",
|
||
|
|
" raise ValueError(\"Out of bag estimation only available\"\n",
|
||
|
|
"ValueError: Out of bag estimation only available if bootstrap=True\n",
|
||
|
|
"\n",
|
||
|
|
" warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
|
||
|
|
"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:548: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
|
||
|
|
"Traceback (most recent call last):\n",
|
||
|
|
" File \"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 531, in _fit_and_score\n",
|
||
|
|
" estimator.fit(X_train, y_train, **fit_params)\n",
|
||
|
|
" File \"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\ensemble\\_forest.py\", line 351, in fit\n",
|
||
|
|
" raise ValueError(\"Out of bag estimation only available\"\n",
|
||
|
|
"ValueError: Out of bag estimation only available if bootstrap=True\n",
|
||
|
|
"\n",
|
||
|
|
" warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
|
||
|
|
"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:548: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
|
||
|
|
"Traceback (most recent call last):\n",
|
||
|
|
" File \"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 531, in _fit_and_score\n",
|
||
|
|
" estimator.fit(X_train, y_train, **fit_params)\n",
|
||
|
|
" File \"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\ensemble\\_forest.py\", line 351, in fit\n",
|
||
|
|
" raise ValueError(\"Out of bag estimation only available\"\n",
|
||
|
|
"ValueError: Out of bag estimation only available if bootstrap=True\n",
|
||
|
|
"\n",
|
||
|
|
" warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
|
||
|
|
"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:548: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
|
||
|
|
"Traceback (most recent call last):\n",
|
||
|
|
" File \"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 531, in _fit_and_score\n",
|
||
|
|
" estimator.fit(X_train, y_train, **fit_params)\n",
|
||
|
|
" File \"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\ensemble\\_forest.py\", line 351, in fit\n",
|
||
|
|
" raise ValueError(\"Out of bag estimation only available\"\n",
|
||
|
|
"ValueError: Out of bag estimation only available if bootstrap=True\n",
|
||
|
|
"\n",
|
||
|
|
" warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
|
||
|
|
"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:548: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
|
||
|
|
"Traceback (most recent call last):\n",
|
||
|
|
" File \"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 531, in _fit_and_score\n",
|
||
|
|
" estimator.fit(X_train, y_train, **fit_params)\n",
|
||
|
|
" File \"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\ensemble\\_forest.py\", line 351, in fit\n",
|
||
|
|
" raise ValueError(\"Out of bag estimation only available\"\n",
|
||
|
|
"ValueError: Out of bag estimation only available if bootstrap=True\n",
|
||
|
|
"\n",
|
||
|
|
" warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
|
||
|
|
"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:548: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
|
||
|
|
"Traceback (most recent call last):\n",
|
||
|
|
" File \"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 531, in _fit_and_score\n",
|
||
|
|
" estimator.fit(X_train, y_train, **fit_params)\n",
|
||
|
|
" File \"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\ensemble\\_forest.py\", line 351, in fit\n",
|
||
|
|
" raise ValueError(\"Out of bag estimation only available\"\n",
|
||
|
|
"ValueError: Out of bag estimation only available if bootstrap=True\n",
|
||
|
|
"\n",
|
||
|
|
" warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
|
||
|
|
"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:548: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
|
||
|
|
"Traceback (most recent call last):\n",
|
||
|
|
" File \"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 531, in _fit_and_score\n",
|
||
|
|
" estimator.fit(X_train, y_train, **fit_params)\n",
|
||
|
|
" File \"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\ensemble\\_forest.py\", line 351, in fit\n",
|
||
|
|
" raise ValueError(\"Out of bag estimation only available\"\n",
|
||
|
|
"ValueError: Out of bag estimation only available if bootstrap=True\n",
|
||
|
|
"\n",
|
||
|
|
" warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
|
||
|
|
"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:548: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
|
||
|
|
"Traceback (most recent call last):\n",
|
||
|
|
" File \"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 531, in _fit_and_score\n",
|
||
|
|
" estimator.fit(X_train, y_train, **fit_params)\n",
|
||
|
|
" File \"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\ensemble\\_forest.py\", line 351, in fit\n",
|
||
|
|
" raise ValueError(\"Out of bag estimation only available\"\n",
|
||
|
|
"ValueError: Out of bag estimation only available if bootstrap=True\n",
|
||
|
|
"\n",
|
||
|
|
" warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
|
||
|
|
"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:548: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
|
||
|
|
"Traceback (most recent call last):\n",
|
||
|
|
" File \"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 531, in _fit_and_score\n",
|
||
|
|
" estimator.fit(X_train, y_train, **fit_params)\n",
|
||
|
|
" File \"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\ensemble\\_forest.py\", line 351, in fit\n",
|
||
|
|
" raise ValueError(\"Out of bag estimation only available\"\n",
|
||
|
|
"ValueError: Out of bag estimation only available if bootstrap=True\n",
|
||
|
|
"\n",
|
||
|
|
" warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
|
||
|
|
"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:548: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
|
||
|
|
"Traceback (most recent call last):\n",
|
||
|
|
" File \"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 531, in _fit_and_score\n",
|
||
|
|
" estimator.fit(X_train, y_train, **fit_params)\n",
|
||
|
|
" File \"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\ensemble\\_forest.py\", line 351, in fit\n",
|
||
|
|
" raise ValueError(\"Out of bag estimation only available\"\n",
|
||
|
|
"ValueError: Out of bag estimation only available if bootstrap=True\n",
|
||
|
|
"\n",
|
||
|
|
" warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
|
||
|
|
"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:548: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
|
||
|
|
"Traceback (most recent call last):\n",
|
||
|
|
" File \"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 531, in _fit_and_score\n",
|
||
|
|
" estimator.fit(X_train, y_train, **fit_params)\n",
|
||
|
|
" File \"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\ensemble\\_forest.py\", line 351, in fit\n",
|
||
|
|
" raise ValueError(\"Out of bag estimation only available\"\n",
|
||
|
|
"ValueError: Out of bag estimation only available if bootstrap=True\n",
|
||
|
|
"\n",
|
||
|
|
" warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
|
||
|
|
"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:548: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
|
||
|
|
"Traceback (most recent call last):\n",
|
||
|
|
" File \"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 531, in _fit_and_score\n",
|
||
|
|
" estimator.fit(X_train, y_train, **fit_params)\n",
|
||
|
|
" File \"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\ensemble\\_forest.py\", line 351, in fit\n",
|
||
|
|
" raise ValueError(\"Out of bag estimation only available\"\n",
|
||
|
|
"ValueError: Out of bag estimation only available if bootstrap=True\n",
|
||
|
|
"\n",
|
||
|
|
" warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
|
||
|
|
"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:548: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
|
||
|
|
"Traceback (most recent call last):\n",
|
||
|
|
" File \"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 531, in _fit_and_score\n",
|
||
|
|
" estimator.fit(X_train, y_train, **fit_params)\n",
|
||
|
|
" File \"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\ensemble\\_forest.py\", line 351, in fit\n",
|
||
|
|
" raise ValueError(\"Out of bag estimation only available\"\n",
|
||
|
|
"ValueError: Out of bag estimation only available if bootstrap=True\n",
|
||
|
|
"\n",
|
||
|
|
" warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
|
||
|
|
"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:548: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
|
||
|
|
"Traceback (most recent call last):\n",
|
||
|
|
" File \"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 531, in _fit_and_score\n",
|
||
|
|
" estimator.fit(X_train, y_train, **fit_params)\n",
|
||
|
|
" File \"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\ensemble\\_forest.py\", line 351, in fit\n",
|
||
|
|
" raise ValueError(\"Out of bag estimation only available\"\n",
|
||
|
|
"ValueError: Out of bag estimation only available if bootstrap=True\n",
|
||
|
|
"\n",
|
||
|
|
" warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
|
||
|
|
"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:548: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
|
||
|
|
"Traceback (most recent call last):\n",
|
||
|
|
" File \"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 531, in _fit_and_score\n",
|
||
|
|
" estimator.fit(X_train, y_train, **fit_params)\n",
|
||
|
|
" File \"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\ensemble\\_forest.py\", line 351, in fit\n",
|
||
|
|
" raise ValueError(\"Out of bag estimation only available\"\n",
|
||
|
|
"ValueError: Out of bag estimation only available if bootstrap=True\n",
|
||
|
|
"\n",
|
||
|
|
" warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
|
||
|
|
"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:548: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
|
||
|
|
"Traceback (most recent call last):\n",
|
||
|
|
" File \"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 531, in _fit_and_score\n",
|
||
|
|
" estimator.fit(X_train, y_train, **fit_params)\n",
|
||
|
|
" File \"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\ensemble\\_forest.py\", line 351, in fit\n",
|
||
|
|
" raise ValueError(\"Out of bag estimation only available\"\n",
|
||
|
|
"ValueError: Out of bag estimation only available if bootstrap=True\n",
|
||
|
|
"\n",
|
||
|
|
" warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
|
||
|
|
"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:548: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
|
||
|
|
"Traceback (most recent call last):\n",
|
||
|
|
" File \"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 531, in _fit_and_score\n",
|
||
|
|
" estimator.fit(X_train, y_train, **fit_params)\n",
|
||
|
|
" File \"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\ensemble\\_forest.py\", line 351, in fit\n",
|
||
|
|
" raise ValueError(\"Out of bag estimation only available\"\n",
|
||
|
|
"ValueError: Out of bag estimation only available if bootstrap=True\n",
|
||
|
|
"\n",
|
||
|
|
" warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
|
||
|
|
"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:548: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
|
||
|
|
"Traceback (most recent call last):\n",
|
||
|
|
" File \"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 531, in _fit_and_score\n",
|
||
|
|
" estimator.fit(X_train, y_train, **fit_params)\n",
|
||
|
|
" File \"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\ensemble\\_forest.py\", line 351, in fit\n",
|
||
|
|
" raise ValueError(\"Out of bag estimation only available\"\n",
|
||
|
|
"ValueError: Out of bag estimation only available if bootstrap=True\n",
|
||
|
|
"\n",
|
||
|
|
" warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
|
||
|
|
"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:548: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
|
||
|
|
"Traceback (most recent call last):\n",
|
||
|
|
" File \"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 531, in _fit_and_score\n",
|
||
|
|
" estimator.fit(X_train, y_train, **fit_params)\n",
|
||
|
|
" File \"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\ensemble\\_forest.py\", line 351, in fit\n",
|
||
|
|
" raise ValueError(\"Out of bag estimation only available\"\n",
|
||
|
|
"ValueError: Out of bag estimation only available if bootstrap=True\n",
|
||
|
|
"\n",
|
||
|
|
" warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
|
||
|
|
"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:548: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
|
||
|
|
"Traceback (most recent call last):\n",
|
||
|
|
" File \"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 531, in _fit_and_score\n",
|
||
|
|
" estimator.fit(X_train, y_train, **fit_params)\n",
|
||
|
|
" File \"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\ensemble\\_forest.py\", line 351, in fit\n",
|
||
|
|
" raise ValueError(\"Out of bag estimation only available\"\n",
|
||
|
|
"ValueError: Out of bag estimation only available if bootstrap=True\n",
|
||
|
|
"\n",
|
||
|
|
" warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
|
||
|
|
"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:548: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
|
||
|
|
"Traceback (most recent call last):\n",
|
||
|
|
" File \"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 531, in _fit_and_score\n",
|
||
|
|
" estimator.fit(X_train, y_train, **fit_params)\n",
|
||
|
|
" File \"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\ensemble\\_forest.py\", line 351, in fit\n",
|
||
|
|
" raise ValueError(\"Out of bag estimation only available\"\n",
|
||
|
|
"ValueError: Out of bag estimation only available if bootstrap=True\n",
|
||
|
|
"\n",
|
||
|
|
" warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
|
||
|
|
"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:548: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
|
||
|
|
"Traceback (most recent call last):\n",
|
||
|
|
" File \"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 531, in _fit_and_score\n",
|
||
|
|
" estimator.fit(X_train, y_train, **fit_params)\n",
|
||
|
|
" File \"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\ensemble\\_forest.py\", line 351, in fit\n",
|
||
|
|
" raise ValueError(\"Out of bag estimation only available\"\n",
|
||
|
|
"ValueError: Out of bag estimation only available if bootstrap=True\n",
|
||
|
|
"\n",
|
||
|
|
" warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
|
||
|
|
"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:548: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
|
||
|
|
"Traceback (most recent call last):\n",
|
||
|
|
" File \"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 531, in _fit_and_score\n",
|
||
|
|
" estimator.fit(X_train, y_train, **fit_params)\n",
|
||
|
|
" File \"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\ensemble\\_forest.py\", line 351, in fit\n",
|
||
|
|
" raise ValueError(\"Out of bag estimation only available\"\n",
|
||
|
|
"ValueError: Out of bag estimation only available if bootstrap=True\n",
|
||
|
|
"\n",
|
||
|
|
" warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
|
||
|
|
"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:548: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
|
||
|
|
"Traceback (most recent call last):\n",
|
||
|
|
" File \"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 531, in _fit_and_score\n",
|
||
|
|
" estimator.fit(X_train, y_train, **fit_params)\n",
|
||
|
|
" File \"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\ensemble\\_forest.py\", line 351, in fit\n",
|
||
|
|
" raise ValueError(\"Out of bag estimation only available\"\n",
|
||
|
|
"ValueError: Out of bag estimation only available if bootstrap=True\n",
|
||
|
|
"\n",
|
||
|
|
" warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
|
||
|
|
"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:548: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
|
||
|
|
"Traceback (most recent call last):\n",
|
||
|
|
" File \"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 531, in _fit_and_score\n",
|
||
|
|
" estimator.fit(X_train, y_train, **fit_params)\n",
|
||
|
|
" File \"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\ensemble\\_forest.py\", line 351, in fit\n",
|
||
|
|
" raise ValueError(\"Out of bag estimation only available\"\n",
|
||
|
|
"ValueError: Out of bag estimation only available if bootstrap=True\n",
|
||
|
|
"\n",
|
||
|
|
" warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
|
||
|
|
"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:548: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
|
||
|
|
"Traceback (most recent call last):\n",
|
||
|
|
" File \"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 531, in _fit_and_score\n",
|
||
|
|
" estimator.fit(X_train, y_train, **fit_params)\n",
|
||
|
|
" File \"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\ensemble\\_forest.py\", line 351, in fit\n",
|
||
|
|
" raise ValueError(\"Out of bag estimation only available\"\n",
|
||
|
|
"ValueError: Out of bag estimation only available if bootstrap=True\n",
|
||
|
|
"\n",
|
||
|
|
" warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
|
||
|
|
"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:548: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
|
||
|
|
"Traceback (most recent call last):\n",
|
||
|
|
" File \"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 531, in _fit_and_score\n",
|
||
|
|
" estimator.fit(X_train, y_train, **fit_params)\n",
|
||
|
|
" File \"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\ensemble\\_forest.py\", line 351, in fit\n",
|
||
|
|
" raise ValueError(\"Out of bag estimation only available\"\n",
|
||
|
|
"ValueError: Out of bag estimation only available if bootstrap=True\n",
|
||
|
|
"\n",
|
||
|
|
" warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
|
||
|
|
"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:548: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
|
||
|
|
"Traceback (most recent call last):\n",
|
||
|
|
" File \"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 531, in _fit_and_score\n",
|
||
|
|
" estimator.fit(X_train, y_train, **fit_params)\n",
|
||
|
|
" File \"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\ensemble\\_forest.py\", line 351, in fit\n",
|
||
|
|
" raise ValueError(\"Out of bag estimation only available\"\n",
|
||
|
|
"ValueError: Out of bag estimation only available if bootstrap=True\n",
|
||
|
|
"\n",
|
||
|
|
" warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
|
||
|
|
"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:548: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
|
||
|
|
"Traceback (most recent call last):\n",
|
||
|
|
" File \"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 531, in _fit_and_score\n",
|
||
|
|
" estimator.fit(X_train, y_train, **fit_params)\n",
|
||
|
|
" File \"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\ensemble\\_forest.py\", line 351, in fit\n",
|
||
|
|
" raise ValueError(\"Out of bag estimation only available\"\n",
|
||
|
|
"ValueError: Out of bag estimation only available if bootstrap=True\n",
|
||
|
|
"\n",
|
||
|
|
" warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
|
||
|
|
"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:548: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
|
||
|
|
"Traceback (most recent call last):\n",
|
||
|
|
" File \"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 531, in _fit_and_score\n",
|
||
|
|
" estimator.fit(X_train, y_train, **fit_params)\n",
|
||
|
|
" File \"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\ensemble\\_forest.py\", line 351, in fit\n",
|
||
|
|
" raise ValueError(\"Out of bag estimation only available\"\n",
|
||
|
|
"ValueError: Out of bag estimation only available if bootstrap=True\n",
|
||
|
|
"\n",
|
||
|
|
" warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
|
||
|
|
"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:548: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
|
||
|
|
"Traceback (most recent call last):\n",
|
||
|
|
" File \"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 531, in _fit_and_score\n",
|
||
|
|
" estimator.fit(X_train, y_train, **fit_params)\n",
|
||
|
|
" File \"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\ensemble\\_forest.py\", line 351, in fit\n",
|
||
|
|
" raise ValueError(\"Out of bag estimation only available\"\n",
|
||
|
|
"ValueError: Out of bag estimation only available if bootstrap=True\n",
|
||
|
|
"\n",
|
||
|
|
" warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
|
||
|
|
"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:548: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
|
||
|
|
"Traceback (most recent call last):\n",
|
||
|
|
" File \"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 531, in _fit_and_score\n",
|
||
|
|
" estimator.fit(X_train, y_train, **fit_params)\n",
|
||
|
|
" File \"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\ensemble\\_forest.py\", line 351, in fit\n",
|
||
|
|
" raise ValueError(\"Out of bag estimation only available\"\n",
|
||
|
|
"ValueError: Out of bag estimation only available if bootstrap=True\n",
|
||
|
|
"\n",
|
||
|
|
" warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
|
||
|
|
"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:548: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
|
||
|
|
"Traceback (most recent call last):\n",
|
||
|
|
" File \"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 531, in _fit_and_score\n",
|
||
|
|
" estimator.fit(X_train, y_train, **fit_params)\n",
|
||
|
|
" File \"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\ensemble\\_forest.py\", line 351, in fit\n",
|
||
|
|
" raise ValueError(\"Out of bag estimation only available\"\n",
|
||
|
|
"ValueError: Out of bag estimation only available if bootstrap=True\n",
|
||
|
|
"\n",
|
||
|
|
" warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
|
||
|
|
"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:548: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
|
||
|
|
"Traceback (most recent call last):\n",
|
||
|
|
" File \"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 531, in _fit_and_score\n",
|
||
|
|
" estimator.fit(X_train, y_train, **fit_params)\n",
|
||
|
|
" File \"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\ensemble\\_forest.py\", line 351, in fit\n",
|
||
|
|
" raise ValueError(\"Out of bag estimation only available\"\n",
|
||
|
|
"ValueError: Out of bag estimation only available if bootstrap=True\n",
|
||
|
|
"\n",
|
||
|
|
" warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
|
||
|
|
"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:548: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
|
||
|
|
"Traceback (most recent call last):\n",
|
||
|
|
" File \"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 531, in _fit_and_score\n",
|
||
|
|
" estimator.fit(X_train, y_train, **fit_params)\n",
|
||
|
|
" File \"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\ensemble\\_forest.py\", line 351, in fit\n",
|
||
|
|
" raise ValueError(\"Out of bag estimation only available\"\n",
|
||
|
|
"ValueError: Out of bag estimation only available if bootstrap=True\n",
|
||
|
|
"\n",
|
||
|
|
" warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
|
||
|
|
"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:548: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
|
||
|
|
"Traceback (most recent call last):\n",
|
||
|
|
" File \"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 531, in _fit_and_score\n",
|
||
|
|
" estimator.fit(X_train, y_train, **fit_params)\n",
|
||
|
|
" File \"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\ensemble\\_forest.py\", line 351, in fit\n",
|
||
|
|
" raise ValueError(\"Out of bag estimation only available\"\n",
|
||
|
|
"ValueError: Out of bag estimation only available if bootstrap=True\n",
|
||
|
|
"\n",
|
||
|
|
" warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
|
||
|
|
"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:548: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
|
||
|
|
"Traceback (most recent call last):\n",
|
||
|
|
" File \"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 531, in _fit_and_score\n",
|
||
|
|
" estimator.fit(X_train, y_train, **fit_params)\n",
|
||
|
|
" File \"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\ensemble\\_forest.py\", line 351, in fit\n",
|
||
|
|
" raise ValueError(\"Out of bag estimation only available\"\n",
|
||
|
|
"ValueError: Out of bag estimation only available if bootstrap=True\n",
|
||
|
|
"\n",
|
||
|
|
" warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
|
||
|
|
"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:548: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
|
||
|
|
"Traceback (most recent call last):\n",
|
||
|
|
" File \"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 531, in _fit_and_score\n",
|
||
|
|
" estimator.fit(X_train, y_train, **fit_params)\n",
|
||
|
|
" File \"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\ensemble\\_forest.py\", line 351, in fit\n",
|
||
|
|
" raise ValueError(\"Out of bag estimation only available\"\n",
|
||
|
|
"ValueError: Out of bag estimation only available if bootstrap=True\n",
|
||
|
|
"\n",
|
||
|
|
" warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
|
||
|
|
"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:548: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
|
||
|
|
"Traceback (most recent call last):\n",
|
||
|
|
" File \"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 531, in _fit_and_score\n",
|
||
|
|
" estimator.fit(X_train, y_train, **fit_params)\n",
|
||
|
|
" File \"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\ensemble\\_forest.py\", line 351, in fit\n",
|
||
|
|
" raise ValueError(\"Out of bag estimation only available\"\n",
|
||
|
|
"ValueError: Out of bag estimation only available if bootstrap=True\n",
|
||
|
|
"\n",
|
||
|
|
" warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
|
||
|
|
"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:548: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
|
||
|
|
"Traceback (most recent call last):\n",
|
||
|
|
" File \"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 531, in _fit_and_score\n",
|
||
|
|
" estimator.fit(X_train, y_train, **fit_params)\n",
|
||
|
|
" File \"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\ensemble\\_forest.py\", line 351, in fit\n",
|
||
|
|
" raise ValueError(\"Out of bag estimation only available\"\n",
|
||
|
|
"ValueError: Out of bag estimation only available if bootstrap=True\n",
|
||
|
|
"\n",
|
||
|
|
" warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
|
||
|
|
"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:548: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
|
||
|
|
"Traceback (most recent call last):\n",
|
||
|
|
" File \"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 531, in _fit_and_score\n",
|
||
|
|
" estimator.fit(X_train, y_train, **fit_params)\n",
|
||
|
|
" File \"c:\\users\\marcial\\anaconda_new\\envs\\ml_master\\lib\\site-packages\\sklearn\\ensemble\\_forest.py\", line 351, in fit\n",
|
||
|
|
" raise ValueError(\"Out of bag estimation only available\"\n",
|
||
|
|
"ValueError: Out of bag estimation only available if bootstrap=True\n",
|
||
|
|
"\n",
|
||
|
|
" warnings.warn(\"Estimator fit failed. The score on this train-test\"\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"GridSearchCV(estimator=RandomForestClassifier(oob_score=True),\n",
|
||
|
|
" param_grid={'bootstrap': [True, False], 'max_features': [2, 3, 4],\n",
|
||
|
|
" 'n_estimators': [64, 100, 128, 200]})"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 66,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"grid.fit(X_train,y_train)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 67,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"{'bootstrap': True, 'max_features': 2, 'n_estimators': 64}"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 67,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"grid.best_params_"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 68,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"predictions = grid.predict(X_test)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 69,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
" precision recall f1-score support\n",
|
||
|
|
"\n",
|
||
|
|
" 0 1.00 0.98 0.99 124\n",
|
||
|
|
" 1 0.98 1.00 0.99 82\n",
|
||
|
|
"\n",
|
||
|
|
" accuracy 0.99 206\n",
|
||
|
|
" macro avg 0.99 0.99 0.99 206\n",
|
||
|
|
"weighted avg 0.99 0.99 0.99 206\n",
|
||
|
|
"\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"print(classification_report(y_test,predictions))"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 70,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x138493358e0>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 70,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 432x288 with 2 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"plot_confusion_matrix(grid,X_test,y_test)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 71,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"True"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 71,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"# No underscore, reports back original oob_score parameter\n",
|
||
|
|
"grid.best_estimator_.oob_score"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 72,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"0.9939965694682675"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 72,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"# With underscore, reports back fitted attribute of oob_score\n",
|
||
|
|
"grid.best_estimator_.oob_score_"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"## Understanding Number of Estimators (Trees)\n",
|
||
|
|
"\n",
|
||
|
|
"Let's plot out error vs. Number of Estimators"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 73,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"from sklearn.metrics import accuracy_score"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 74,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"errors = []\n",
|
||
|
|
"misclassifications = []\n",
|
||
|
|
"\n",
|
||
|
|
"for n in range(1,64):\n",
|
||
|
|
" rfc = RandomForestClassifier( n_estimators=n,bootstrap=True,max_features= 2)\n",
|
||
|
|
" rfc.fit(X_train,y_train)\n",
|
||
|
|
" preds = rfc.predict(X_test)\n",
|
||
|
|
" err = 1 - accuracy_score(preds,y_test)\n",
|
||
|
|
" n_missed = np.sum(preds != y_test) # watch the video to understand this line!!\n",
|
||
|
|
" errors.append(err)\n",
|
||
|
|
" misclassifications.append(n_missed)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 75,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"[<matplotlib.lines.Line2D at 0x13849748310>]"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 75,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 432x288 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"plt.plot(range(1,64),errors)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 76,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"[<matplotlib.lines.Line2D at 0x13849791c10>]"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 76,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 432x288 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"plt.plot(range(1,64),misclassifications)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": []
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"anaconda-cloud": {},
|
||
|
|
"kernelspec": {
|
||
|
|
"display_name": "Python 3",
|
||
|
|
"language": "python",
|
||
|
|
"name": "python3"
|
||
|
|
},
|
||
|
|
"language_info": {
|
||
|
|
"codemirror_mode": {
|
||
|
|
"name": "ipython",
|
||
|
|
"version": 3
|
||
|
|
},
|
||
|
|
"file_extension": ".py",
|
||
|
|
"mimetype": "text/x-python",
|
||
|
|
"name": "python",
|
||
|
|
"nbconvert_exporter": "python",
|
||
|
|
"pygments_lexer": "ipython3",
|
||
|
|
"version": "3.8.5"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"nbformat": 4,
|
||
|
|
"nbformat_minor": 1
|
||
|
|
}
|