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1039 lines
1.9 MiB
1039 lines
1.9 MiB
2 years ago
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{
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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 - Regression\n",
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"## Plus: An Additional Analysis of Various Regression Methods!\n",
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"\n",
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"## The Data\n",
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"\n",
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"We just got hired by a tunnel boring company which uses X-rays in an attempt to know rock density, ideally this will allow them to switch out boring heads on their equipment before having to mine through the rock!\n",
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"\n",
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"<img src='boring.jpg' height=400>\n",
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"\n",
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"They have given us some lab test results of signal strength returned in nHz to their sensors for various rock density types tested. You will notice it has almost a sine wave like relationship, where signal strength oscillates based off the density, the researchers are unsure why this is, but"
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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": 178,
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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": 179,
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"metadata": {},
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"outputs": [],
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"source": [
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"df = pd.read_csv(\"../DATA/rock_density_xray.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": 180,
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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>Rebound Signal Strength nHz</th>\n",
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" <th>Rock Density kg/m3</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>72.945124</td>\n",
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" <td>2.456548</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>14.229877</td>\n",
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" <td>2.601719</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>36.597334</td>\n",
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" <td>1.967004</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>9.578899</td>\n",
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" <td>2.300439</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>21.765897</td>\n",
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" <td>2.452374</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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" Rebound Signal Strength nHz Rock Density kg/m3\n",
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"0 72.945124 2.456548\n",
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"1 14.229877 2.601719\n",
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"2 36.597334 1.967004\n",
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"3 9.578899 2.300439\n",
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"4 21.765897 2.452374"
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]
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},
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"execution_count": 180,
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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.head()"
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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": 181,
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"metadata": {},
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"outputs": [],
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"source": [
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"df.columns=['Signal',\"Density\"]"
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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": 182,
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"metadata": {},
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"outputs": [
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{
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"data": {
|
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"text/plain": [
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"<AxesSubplot:xlabel='Signal', ylabel='Density'>"
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]
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},
|
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"execution_count": 182,
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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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"data": {
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"image/png": "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"text/plain": [
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"<Figure size 2400x1600 with 1 Axes>"
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]
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},
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"metadata": {
|
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"needs_background": "light"
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},
|
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"output_type": "display_data"
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}
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],
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"source": [
|
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"plt.figure(figsize=(12,8),dpi=200)\n",
|
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|
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"sns.scatterplot(x='Signal',y='Density',data=df)"
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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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|
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"---\n",
|
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|
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"---"
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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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"## Splitting the Data\n",
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"\n",
|
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|
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"Let's split the data in order to be able to have a Test set for performance metric evaluation."
|
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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": 198,
|
||
|
|
"metadata": {},
|
||
|
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"outputs": [],
|
||
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"source": [
|
||
|
|
"X = df['Signal'].values.reshape(-1,1) \n",
|
||
|
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"y = df['Density']"
|
||
|
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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": 199,
|
||
|
|
"metadata": {},
|
||
|
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"outputs": [],
|
||
|
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"source": [
|
||
|
|
"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",
|
||
|
|
"execution_count": 200,
|
||
|
|
"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=0.1, random_state=101)"
|
||
|
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]
|
||
|
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},
|
||
|
|
{
|
||
|
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"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
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"-----\n",
|
||
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"\n",
|
||
|
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"# Linear Regression"
|
||
|
|
]
|
||
|
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},
|
||
|
|
{
|
||
|
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"cell_type": "code",
|
||
|
|
"execution_count": 201,
|
||
|
|
"metadata": {},
|
||
|
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"outputs": [],
|
||
|
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"source": [
|
||
|
|
"from sklearn.linear_model import LinearRegression"
|
||
|
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]
|
||
|
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},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 202,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
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"source": [
|
||
|
|
"lr_model = LinearRegression()"
|
||
|
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]
|
||
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},
|
||
|
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{
|
||
|
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"cell_type": "code",
|
||
|
|
"execution_count": 203,
|
||
|
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"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
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"data": {
|
||
|
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"text/plain": [
|
||
|
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"LinearRegression()"
|
||
|
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]
|
||
|
|
},
|
||
|
|
"execution_count": 203,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
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],
|
||
|
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"source": [
|
||
|
|
"lr_model.fit(X_train,y_train)"
|
||
|
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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": 204,
|
||
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"metadata": {},
|
||
|
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"outputs": [],
|
||
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"source": [
|
||
|
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"lr_preds = lr_model.predict(X_test)"
|
||
|
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]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 205,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"from sklearn.metrics import mean_squared_error"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 206,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"0.2570051996584629"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 206,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"np.sqrt(mean_squared_error(y_test,lr_preds))"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"What does the fit look like?"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 208,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"signal_range = np.arange(0,100)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 210,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"lr_output = lr_model.predict(signal_range.reshape(-1,1))"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 214,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"[<matplotlib.lines.Line2D at 0x216f1c9c490>]"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 214,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAB9IAAAU2CAYAAADJT/3yAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAB7CAAAewgFu0HU+AAEAAElEQVR4nOz9fXBr+X3feX7ORQOkhcM4MgiPBJMUE4VHjhJPTeXBUm8ykqsFkvKMjJr2xIQjx4kcCVkkjKeUQtKrTbxZJ7PJSOU1ZrOB98LGVTrxpCyDGT2EXK8NQi3H8k7UtuLJVBJXm2Q2aZo0obUP/DAAdsBzhD77By/Z5OXlJZ6Bg/N+Vd1q8hC/c373Amjid76/7/dreJ4nAAAAAAAAAAAAAABw4cGkJwAAAAAAAAAAAAAAwDQhkA4AAAAAAAAAAAAAwDUE0gEAAAAAAAAAAAAAuIZAOgAAAAAAAAAAAAAA1xBIBwAAAAAAAAAAAADgGgLpAAAAAAAAAAAAAABcQyAdAAAAAAAAAAAAAIBrCKQDAAAAAAAAAAAAAHANgXQAAAAAAAAAAAAAAK4hkA4AAAAAAAAAAAAAwDUE0gEAAAAAAAAAAAAAuIZAOgAAAAAAAAAAAAAA1xBIBwAAAAAAAAAAAADgGgLpAAAAAAAAAAAAAABcQyAdAAAAAAAAAAAAAIBrCKQDAAAAAAAAAAAAAHDNc5OeAEbPMIw5Sd/2+NvfktSZ4HQAAAAAAAAAAAAAYJhCkuKPv/63nuedD3pCAunB8G2SvjrpSQAAAAAAAAAAAADAiP1JSf9q0JNQ2h0AAAAAAAAAAAAAgGvISA+G37r84pd/+Zf19re/fZJzAQAAAAAAAAAAAIChqdVq+vZv//bLb3/rWY/tVmAD6YZh/AlJ/4WkPy3p3bqome9KOpP0P0n6tOd5/+8hXu+PSspKer+kd0ial/R7kn5V0q6kkud5jWFd7wlXPdHf/va3a2lpaUSXAQAAAAAAAAAAAICJ6tz/kPsFMpBuGMaXJf3nT/lRRNLa4z8fMQzjJyVlPM9zBrze/0HS39NFk/vrFnURWH+/pI8bhpHyPO9/GeRaAAAAAAAAAAAAAIDBBDKQLinx+L9nkv6ZpF+U9Ou6CHQ/Lykn6Vsk/XlJYUkf7vdChmH8WUmffPytI+nHJH1Rki3pnZL+ii6y4pcl/ZxhGN/qed7v9ns9AAAAAAAAAAAAAMBgghpI/zVJf1PSZz3PezK1/1XDMP4HXZR3tyT9WcMwip7nfbnPa/2ta19/t+d5P3Pt+1+W9BnDMD4r6bsl/SeSPibp/9rntQAAAAAAAAAAAAAAA3ow6QlMgud5H/I8b+cpQfTLn9u6yEq/9Gf6uY5hGL9P0h95/O3//EQQ/bq/c+3r5/u5FgAAAAAAAAAAAABgOAIZSO/Sz1/7+p19niNy7ev/8IzH/X/uGAMAAAAAAAAAAAAAGDMC6Xebu/b1UzPX7/M4s/23H3/7B5/x0OuB+oN+rgUAAAAAAAAAAAAAGI6g9kjvxvuvff3aAOcp6qIf+x8zDOODnuf93FMe8396/N+vS3rU6wUMw1i65yFv6/WcAAAAAAAAAAAAABBUBNKfwjCMB5I+ce3QzgCn+/uS/rikTUlfMAyjIOkVSbYustT/si6C9h1Jf9XzvF/r4xonA8wPAAAAAAAAAAAAAHANgfSn+2uSvv3x15/zPO9X+j2R53ktwzA+JOnP6yIzPff4z3Wfk/RJz/O+2u91AAAAAAAAAAAAAADDQSD9CYZhvF/SJx9/+5u6yBgf1LdL+nO6u0/6uqTfMgzj0PO83+vj/Mv3/PxtkgjSAwAAAAAAAAAAAEAXCKRfYxjGH5H0eV38u7QlfY/neb854Dn/jKR/KmlO0r+R9H+W9GVJDV0EwNO66JH+v5f0PsMwkp7nnfVyDc/zTu+ZQx8zBwAAAAAAAAAAAIBgejDpCUwLwzD+gKR9SW/VRb/y7/U878sDnvM/kfSPdRFE/1VJ/zvP877ged5ve57nep73HzzP++8kfZckT9IflvQPB7kmAAAAAAAAAAAAAGAwBNIlGYaRkPRFSQldBLT/oud5/3wIp/5eSdHHX/99z/NaT3uQ53mvSHrl8bf/lWEYbx3CtQEAAAAAAAAAAAAAfQh8IN0wjEVJVb3Zv/wHPc/7ySGd/g9f+/p/vuexv/L4vw8kWUO6PgAAAAAAAAAAAACgR4EOpBuG8Y2SKpLe/fjQJzzP+7EhXuLr176+rx99+I5xAAAAAAAAAAAAAIAxCmwg3TCMt0j6GUl/7PGhv+d53qeGfJn/eO3r//yex77v8X89Sa8PeR4AAAAAAAAAAAAAgC4FMpBuGEZE0ucl/anHh/6B53k/1Md5PmIYhvf4zw8/5SE/o4vAuCT9LcMwvuWO8/wlSX/i8bevep5X73UuAAAAAAAAAAAAAIDhuK/c+Kz6jKSNx19/SdKnDcP4o894vON53mGvF/E879cMw3hZ0l+U9C2S/rVhGP83Sb8oqSFpWdL3Svrw4yEdSX+z1+sAAAAAAAAAAAAAAIYnqIH077729QuS/s09jz+WtNrntf6KpKiktKS4pL93x+Nakv6S53n/os/rAAAAAAAAAAAAAACGIKiB9LHxPO9c0vcahvHjkj4i6b26yE6fk/S/SjqQ9EVJP+F53umk5gkAAAAAAAAAAAAAuBDIQLrnecaQzvOPJf3jLh/785J+fhjXBQAAAAAAAAAAAACMzoNJTwAAAAAAAAAAAAAAgGlCIB0AAAAAAAAAAAAAgGsIpAMAAAAAAAAAAAAAcA2BdAAAAAAAAAAAAAAAriGQDgAAAAAAAAAAAADANQTSAQAAAAAAAAAAAAC4hkA6AAAAAAAAAAAAAADXEEgHAAAAAAAAAAAAAOAaAukAAAAAAAAAAAAAAFxDIB0AAAAAAAAAAAAAgGsIpAMAAAAAAAAAAAAAcA2BdAAAAAAAAAAAAAAAriGQDgAAAAAAAAAAAADANQTSAQAAAAAAAAAAAAC4hkA6AAAAAAAAAAAAAADXEEgHAAAAAAAAAAAAAOAaAukAAAAAAAAAAAAAAFxDIB0AAAAAAAAAAAAAgGsIpAMAAAAAAAAAAAAAcA2BdAAAAAAAAAAAAAAAriGQDgAAAAAAAAAAAADANQTSAQAAAAAAAAAAAAC4hkA6AAAAAAAAAAAAAADXEEgHAGDEHMeRJNm2rePjY9m2feM4AAAAAAAAAACYLgTSAQAYEdd11Wg0VCgUZFmW4vG4VldXFY/HZVmWCoWCGo2GXNed9FQBAAAAAAAAAMA1BNIBABiBTqejarWqRCKhXC6no6OjGz8/OjpSLpdTIpFQtVpVp9OZ0EwBAAAAAAAAAMCTCKQDADBkruuqUqkolUqp2Ww+87HNZlOpVEr7+/tkpgMAAAAAgMCjRR4AYFoQSAcAYMja7bbS6XTXWeadTkdbW1s6Pz8f8cwAAAAAAACmEy3yAADThkA6AABD5DiOSqXSvZnoT2o2myqVSuyuBgAAAABgAsiCnixa5AEAphGBdAAAhigSiahYLPY1tlgsKhKJDHlGAAAAAADgLmRBTx4t8gAA08rwPG/Sc8CIGYaxJOlEkk5OTrS0tDThGQHA7LJtW/F4fKDxsVhsiDMCAAAAAABP0+l0VKlUlE6nnxnANU1T5XJZm5ubCoVCY5xhMDQaDSUSiZ6q+5mmqVqtJtM0RzgzAICfnJ6eanl5+fLbZc/zTgc9JxnpAAAMUavVmuh4AAAAAABwP7KgpwMt8gAA04xAOgAAQxSNRic6HgAAAAAA3K/dbiudTnfda7vT6Whra0vn5+cjnlmw0CIPADDNCKQDADBEi4uLWltb62usZVmUdQcAAAAAYMTIgp4etm3r6Oior7GHh4eq1+tDnhEAAG8ikA4AwBA5jqNsNtvX2Gw2y2IcAAAAAIARIwt6etAiDwAwzQikAwAwRJFIRJlMRqZp9jTONE1lMhkW4wAAAAAAjBhZ0NODFnkAgGlGIB0AgCGbn59XuVxWKBTq6vGhUEg7Ozuam5sb8cwAAAAAAABZ0NODFnkAgGlGIB0AgCELh8Pa3Nz
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 2400x1600 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"plt.figure(figsize=(12,8),dpi=200)\n",
|
||
|
|
"sns.scatterplot(x='Signal',y='Density',data=df,color='black')\n",
|
||
|
|
"plt.plot(signal_range,lr_output)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"---\n",
|
||
|
|
"\n",
|
||
|
|
"# Polynomial Regression"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"## Attempting with a Polynomial Regression Model\n",
|
||
|
|
"\n",
|
||
|
|
"Let's explore why our standard regression approach of a polynomial could be difficult to fit here, keep in mind, we're in a fortunate situation where we can easily visualize results of y vs x."
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"## Function to Help Run Models"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 223,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"from sklearn.linear_model import LinearRegression\n",
|
||
|
|
"model = LinearRegression()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 224,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"LinearRegression()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 224,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"model.fit(poly_features,y_train)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 266,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"def run_model(model,X_train,y_train,X_test,y_test):\n",
|
||
|
|
" \n",
|
||
|
|
" # Fit Model\n",
|
||
|
|
" model.fit(X_train,y_train)\n",
|
||
|
|
" \n",
|
||
|
|
" # Get Metrics\n",
|
||
|
|
" \n",
|
||
|
|
" preds = model.predict(X_test)\n",
|
||
|
|
" \n",
|
||
|
|
" rmse = np.sqrt(mean_squared_error(y_test,preds))\n",
|
||
|
|
" print(f'RMSE : {rmse}')\n",
|
||
|
|
" \n",
|
||
|
|
" # Plot results\n",
|
||
|
|
" signal_range = np.arange(0,100)\n",
|
||
|
|
" output = model.predict(signal_range.reshape(-1,1))\n",
|
||
|
|
" \n",
|
||
|
|
" \n",
|
||
|
|
" plt.figure(figsize=(12,6),dpi=150)\n",
|
||
|
|
" sns.scatterplot(x='Signal',y='Density',data=df,color='black')\n",
|
||
|
|
" plt.plot(signal_range,output)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 267,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"RMSE : 0.15234870286353372\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 1800x900 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"run_model(model,X_train,y_train,X_test,y_test)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"## Pipeline for Poly Orders"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 268,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"from sklearn.pipeline import make_pipeline"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 269,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"from sklearn.preprocessing import PolynomialFeatures"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 270,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"pipe = make_pipeline(PolynomialFeatures(2),LinearRegression())"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 271,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"RMSE : 0.2817309563725596\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 1800x900 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"run_model(pipe,X_train,y_train,X_test,y_test)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"## Comparing Various Polynomial Orders"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 272,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"RMSE : 0.1417947898442399\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 1800x900 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"pipe = make_pipeline(PolynomialFeatures(10),LinearRegression())\n",
|
||
|
|
"run_model(pipe,X_train,y_train,X_test,y_test)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"----\n",
|
||
|
|
"\n",
|
||
|
|
"## KNN Regression"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 273,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"from sklearn.neighbors import KNeighborsRegressor"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 274,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"RMSE : 0.15234870286353372\n",
|
||
|
|
"RMSE : 0.13730685016923655\n",
|
||
|
|
"RMSE : 0.13277855732740926\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 1800x900 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 1800x900 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 1800x900 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"preds = {}\n",
|
||
|
|
"k_values = [1,5,10]\n",
|
||
|
|
"for n in k_values:\n",
|
||
|
|
" \n",
|
||
|
|
" \n",
|
||
|
|
" model = KNeighborsRegressor(n_neighbors=n)\n",
|
||
|
|
" run_model(model,X_train,y_train,X_test,y_test)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"---\n",
|
||
|
|
"## Decision Tree Regression"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 275,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"from sklearn.tree import DecisionTreeRegressor"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 276,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"RMSE : 0.15234870286353372\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 1800x900 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"model = DecisionTreeRegressor()\n",
|
||
|
|
"\n",
|
||
|
|
"run_model(model,X_train,y_train,X_test,y_test)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 277,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"270"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 277,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"model.get_n_leaves()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"----\n",
|
||
|
|
"\n",
|
||
|
|
"## Support Vector Regression"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 282,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"from sklearn.svm import SVR"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 283,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"from sklearn.model_selection import GridSearchCV"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 284,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"param_grid = {'C':[0.01,0.1,1,5,10,100,1000],'gamma':['auto','scale']}\n",
|
||
|
|
"svr = SVR()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 285,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"grid = GridSearchCV(svr,param_grid)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 286,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"RMSE : 0.12634668775105407\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 1800x900 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"run_model(grid,X_train,y_train,X_test,y_test)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 287,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"SVR(C=1000)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 287,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"grid.best_estimator_"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"---\n",
|
||
|
|
"\n",
|
||
|
|
"## Random Forest Regression"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 288,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"from sklearn.ensemble import RandomForestRegressor"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 289,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"# help(RandomForestRegressor)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 290,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"RMSE : 0.1417613358931285\n",
|
||
|
|
"RMSE : 0.133281449397454\n",
|
||
|
|
"RMSE : 0.13699094997283662\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 1800x900 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 1800x900 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 1800x900 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"trees = [10,50,100]\n",
|
||
|
|
"for n in trees:\n",
|
||
|
|
" \n",
|
||
|
|
" model = RandomForestRegressor(n_estimators=n)\n",
|
||
|
|
" \n",
|
||
|
|
" run_model(model,X_train,y_train,X_test,y_test)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"## Gradient Boosting\n",
|
||
|
|
"\n",
|
||
|
|
"We will cover this in more detail in next section."
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 291,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"from sklearn.ensemble import GradientBoostingRegressor"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 292,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"# help(GradientBoostingRegressor)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 293,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"RMSE : 0.13294148649584667\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 1800x900 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
" \n",
|
||
|
|
"model = GradientBoostingRegressor()\n",
|
||
|
|
"\n",
|
||
|
|
"run_model(model,X_train,y_train,X_test,y_test)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"## Adaboost"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 294,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"from sklearn.ensemble import AdaBoostRegressor"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 295,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"RMSE : 0.13294148649584667\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAABd0AAAMGCAYAAAAHgi0pAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAABcSAAAXEgFnn9JSAAEAAElEQVR4nOzdeVxcd7k/8M+BgQGGwBDIymQnG4SEtE26hCTVWm1tIV3QKl5rG7UJ6q1eTai31+u93t/9WSFxqfdqkvqztW5Y0ZqEqummbUO6xQaSANnIzpAFCBPgAAPDnN8fMGfOwOxzZv+8X6+8mOV7zhzIMHzPc57v8wiSJIGIiIiIiIiIiIiIiAKXEO4DICIiIiIiIiIiIiKKFQy6ExERERERERERERGphEF3IiIiIiIiIiIiIiKVMOhORERERERERERERKQSBt2JiIiIiIiIiIiIiFTCoDsRERERERERERERkUoYdCciIiIiIiIiIiIiUgmD7kREREREREREREREKmHQnYiIiIiIiIiIiIhIJQy6ExERERERERERERGphEF3IiIiIiIiIiIiIiKVMOhORERERERERERERKQSBt2JiIiIiIiIiIiIiFTCoDsRERERERERERERkUqiMuguCEKaIAj3CYLwc0EQTgiCMCgIgigIwmFBEL4tCEK6H/u8UxCEPwuC0CEIwrAgCF2CILwiCML9wfgeiIiIiIiIiIiIiCj2CJIkhfsYfCYIwhcA/Gzs7jEATQAyANwGYBKA4wDWS5J01cv9fQ3ADwFIAN4BcBHALAC3AhAAfFeSpH8L8JgvA0gb2zcRERERERERERERRaZZAPolSZruz8bRGnT/HEYD7D+SJOmY4vEZAP4MYCWAGkmSyr3Y1xSMBsITANwpSdKbiufWAXgFQDKAPEmSzgRwzD1arXbSggUL/N0FEREREREREREREQXZ6dOnYTabeyVJyvBn+6gMursjCMKtAN4GYAaQIUnSkIfx9wKoA/CyJEl3OXl+D4BSAA9JkvT7AI6rOT8/P7+5udnfXRARERERERERERFRkBUUFKClpaVFkqQCf7aPypruHhwe+6oFkO3FeLOX++3y73CIiIiIiIiIiIiIKF7EYtB9/tjXYQDXvBj/PgATgA8LgrBe+cRYeZmPATgFYL+Kx0hEREREREREREREMSgWg+5fHfu6T5Ikj1nskiRdB/B5AFYAfxcEoV4QhN8JglAP4A0ABwF8zFOZGiIiIiIiIiIiIiIiTbgPQE2CIHwcowH0YQD/7u12kiS9KAjC3QB+D2CN4qkejDZSNfpwDK6KtrODKhEREREREREREVGMi5lMd0EQlgD4NQABwFZJkg572ES57TcAvAbgLQDLAaSPff0bgP8C8KLqB0xEREREREREREREMScmMt0FQcgFsA9AFoAfSJL0tA/b3g5gO4BDAD4hSZJ17KmjgiCUAfgHgHsEQbhbkqS/etqfq462Yxnw+d4eFxERERERERERERFFn6jPdBcEYTJGS8DMAfAcgC0+7uKzY1//pAi4AwAkSRqBPct9XSDHSURERERERERERESxL6qD7oIgpAP4K0YzyF8E8EVJkiQfd2MY+3rdxfO2x7N8P0IiIiIiIiIiIiIiiidRG3QXBEELYA+A1QBeBvDpscx0X10e+3qTi+dXjX0958e+iYiIiIiIiIiIiCiORGXQXRCERAA1AD4MYD+AByRJGvKwzVcEQTguCMJT457aPfb1M4Ig3Dtumw0AygFYAfxJjWMnIiIiIiIiIiIiotgVrY1UvwLg/rHbnQB+KgiCs3FbJEnqHLudA2AxgBnjxuwGUAvgEwDqBEH4B4CzAObBnv3+b5IknVDt6ImIiIiIiIiIiIgoJkVr0F1ZX/1+l6OA/8RoUN4lSZIkQRAeArAPwOcALAdQBMAE4C8A/keSpH0BHCsRERERERERERERxQnB976j5A9BEJrz8/Pzm5ubw30oRERERERERERERORCQUEBWlpaWiRJKvBn+6is6U5EREREREREREREFIkYdCciIiIiIiIiIiIiUgmD7kREREREREREREREKmHQnYiIiIiIiIiIiIhIJQy6ExERERERERERERGphEF3IiIiIiIiIiIiIiKVMOhORERERERERERERKQSBt2JiIiIiIiIiIiIiFTCoDsRERERERERERERkUo04T4AIiICLBYLzGYzTCYTuru7kZWVBb1eD61WC42GH9VERERERERERNGCme5ERGEmiiJqa2tRXFwMg8GAwsJCGAwGFBcXo7a2FqIohvsQiYiIiIiIiIjISwy6ExGFkSiK2LJlC8rLy9HY2OjwXGNjI8rLy7F161YG3omIiIiIKKpZLBaIogij0YimpiYYjUaIogiLxRLuQyMiUh2D7kREYWKxWLB3717s3LnT7bgdO3agrq6Ok1EiIiIiogjEYLJnXN1LRPGGQXciojAxm82oqqryamx1dTXMZnOQj4iIiIiIiHzBYLJnXN1LRPGIQXciojAxmUw4fPiwV2MbGhpgMpmCe0BEREREROQ1BpM94+peIopXDLoTEYVJd3e3T+MZdCciIiIiigwMJnuHq3uJKF4x6E5EFCZZWVk+jdfr9cE5ECIiIiIi8gmDyd7h6l4iilcMuhMRhYler0dRUZFXY1euXMmgOxERERFRhGAw2Ttc3UtE8YpBdyKKaxaLBaIowmg0oqmpCUajEaIohmT5p1arRWVlpVdjKysrodVqg3xERERERETkDQaTvcPVvUQUrxh0J6K4JYoiamtrUVxcDIPBgMLCQhgMBhQXF6O2tjboDY80Gg1KS0uxefNmt+MqKipQUlICjUYT1OMhIiIiIiLvMJjsHa7uJaJ4xaA7EcUlURSxZcsWlJeXo7Gx0eG5xsZGlJeXY+vWrUEPvOt0Omzfvh01NTUTJqNFRUWoqanBtm3boNPpgnocRERERETkPQaTvcPVvUQUrxh0J6K4Y7FYsHfvXuzcudPtuB07dqCuri7opWZ0Oh3KyspQX1+PtrY2NDU1oa2tDfX19SgrK2PAnYiIiIgowjCY7B2u7iWieMWgOxHFHbPZjKqqKq/GVldXw2w2B/mIRiejOp0Oubm5KCgoQG5uLnQ6HSedREREREQRiMFk73F1LxHFI0GSpHAfQ1wQBKE5Pz8/v7m5OdyHQhT3jEYjDAaD1+Pb2tqQm5sbxCMiIiIiIqJoJIoi6urqUFVV5VC2sqioCE888QRKSkoYTB5jsVhgNpthMplgMpmg1+uh1+uh1Wrj+qIEEUWmgoICtLS0tEiSVODP9vxUI6K4093d7dN4k8nEoDsREREREU1gKxVZUlIiB5NzcnKQkZGBpKQkBpMVNBqNwwpfIqJYxk9/Ioo7WVlZPo2P16ZHRERERETkmS2YrNVqodfrYTKZcPr0aWRlZTGTm4goTrGmOxHFBIvFAlEUYTQa0dTUBKPRCFEUnTZB1ev1E2oJurJy5UoG3YmIiIiIyC1RFFFbW4vi4mIYDAYUFhbCYDCguLgYtbW1EEUx3IdIREQhxKA7EUU9Xye4Wq0WlZWVXu27srISWq02GIdNREREREQxQBRFbNmyBeXl5Q513QGgsbER5eXl2Lp1KwPvRERxhEF3Iopq/kxwNRoNSktLsXnzZrf7rqioQElJCZeCEhERERGRUxaLBXv37sXOnTvdjtuxYwfq6uqcrsQlIqLYw6A7EUWtQCa4Op0O27dvR01NzYRSM0VFRaipqcG2bdug0+mCcehERERERBQDzGYzqqqqvBpbXV0Ns9kc5CMiIqJIIEiSFO5jiAuCIDTn5+fnNzc3h/tQiGKGKIpYs2YNDh8+7HHsypUrsX///glBdIvFArPZDJPJBJPJBL1ez2ZHRERERETkFaPRCIPB4PX4trY25ObmBvGIiIhIDQUFBWhpaWmRJKnAn+0ZUSKiqGUymbwKuANAQ0MDTCbThKC7RqOBRqOBTqfj5JeIiIiIiHzS3d3t03iTycTzDiKiOMCgOxFFLU5wI5Ny9UB3dzeysrK4eoCIiIiIYlJWVpZP4/V6vaqvz7k3EVFkYk13Iopa4Z7
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 1800x900 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"model = GradientBoostingRegressor()\n",
|
||
|
|
"\n",
|
||
|
|
"run_model(model,X_train,y_train,X_test,y_test)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"----"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"anaconda-cloud": {},
|
||
|
|
"kernelspec": {
|
||
|
|
"display_name": "Python 3",
|
||
|
|
"language": "python",
|
||
|
|
"name": "python3"
|
||
|
|
},
|
||
|
|
"language_info": {
|
||
|
|
"codemirror_mode": {
|
||
|
|
"name": "ipython",
|
||
|
|
"version": 3
|
||
|
|
},
|
||
|
|
"file_extension": ".py",
|
||
|
|
"mimetype": "text/x-python",
|
||
|
|
"name": "python",
|
||
|
|
"nbconvert_exporter": "python",
|
||
|
|
"pygments_lexer": "ipython3",
|
||
|
|
"version": "3.8.5"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"nbformat": 4,
|
||
|
|
"nbformat_minor": 1
|
||
|
|
}
|