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2077 lines
522 KiB
2077 lines
522 KiB
{
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"cells": [
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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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"___\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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" | - If float, then `max_features` is a fraction and\n",
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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",
|
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" | - If \"sqrt\", then `max_features=sqrt(n_features)` (same as \"auto\").\n",
|
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" | - If \"log2\", then `max_features=log2(n_features)`.\n",
|
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" | - If None, then `max_features=n_features`.\n",
|
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" | \n",
|
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" | Note: the search for a split does not stop until at least one\n",
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" | valid partition of the node samples is found, even if it requires to\n",
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" | effectively inspect more than ``max_features`` features.\n",
|
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" | \n",
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" | max_leaf_nodes : int, default=None\n",
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" | Grow trees with ``max_leaf_nodes`` in best-first fashion.\n",
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" | Best nodes are defined as relative reduction in impurity.\n",
|
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" | If None then unlimited number of leaf nodes.\n",
|
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" | \n",
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" | min_impurity_decrease : float, default=0.0\n",
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" | A node will be split if this split induces a decrease of the impurity\n",
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" | greater than or equal to this value.\n",
|
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" | \n",
|
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" | The weighted impurity decrease equation is the following::\n",
|
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" | \n",
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" | N_t / N * (impurity - N_t_R / N_t * right_impurity\n",
|
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" | - N_t_L / N_t * left_impurity)\n",
|
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" | \n",
|
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" | where ``N`` is the total number of samples, ``N_t`` is the number of\n",
|
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" | samples at the current node, ``N_t_L`` is the number of samples in the\n",
|
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" | left child, and ``N_t_R`` is the number of samples in the right child.\n",
|
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" | \n",
|
|
" | ``N``, ``N_t``, ``N_t_R`` and ``N_t_L`` all refer to the weighted sum,\n",
|
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" | if ``sample_weight`` is passed.\n",
|
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" | \n",
|
|
" | .. versionadded:: 0.19\n",
|
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" | \n",
|
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" | min_impurity_split : float, default=None\n",
|
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" | Threshold for early stopping in tree growth. A node will split\n",
|
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" | 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",
|
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" | \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",
|
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" | \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",
|
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" | [{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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\n",
|
|
"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": 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\n",
|
|
"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",
|
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" .dataframe tbody tr th:only-of-type {\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",
|
|
" <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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\n",
|
|
"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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\n",
|
|
"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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\n",
|
|
"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": {
|
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"image/png": 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\n",
|
|
"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",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.5"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 1
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}
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