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Created on Skills Network Labs
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<center>\n",
" <img src=\"https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/Logos/organization_logo/organization_logo.png\" width=\"300\" alt=\"cognitiveclass.ai logo\" />\n",
"</center>\n",
"\n",
"# Model Evaluation and Refinement\n",
"\n",
"Estimated time needed: **30** minutes\n",
"\n",
"## Objectives\n",
"\n",
"After completing this lab you will be able to:\n",
"\n",
"- Evaluate and refine prediction models\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<h1>Table of content</h1>\n",
"<ul>\n",
" <li><a href=\"#ref1\">Model Evaluation </a></li>\n",
" <li><a href=\"#ref2\">Over-fitting, Under-fitting and Model Selection </a></li>\n",
" <li><a href=\"#ref3\">Ridge Regression </a></li>\n",
" <li><a href=\"#ref4\">Grid Search</a></li>\n",
"</ul>\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This dataset was hosted on IBM Cloud object click <a href=\"https://cocl.us/DA101EN_object_storage\">HERE</a> for free storage.\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"\n",
"# Import clean data \n",
"path = 'https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DA0101EN-SkillsNetwork/labs/Data%20files/module_5_auto.csv'\n",
"df = pd.read_csv(path)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"df.to_csv('module_5_auto.csv')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" First lets only use numeric data \n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Unnamed: 0</th>\n",
" <th>Unnamed: 0.1</th>\n",
" <th>symboling</th>\n",
" <th>normalized-losses</th>\n",
" <th>wheel-base</th>\n",
" <th>length</th>\n",
" <th>width</th>\n",
" <th>height</th>\n",
" <th>curb-weight</th>\n",
" <th>engine-size</th>\n",
" <th>...</th>\n",
" <th>stroke</th>\n",
" <th>compression-ratio</th>\n",
" <th>horsepower</th>\n",
" <th>peak-rpm</th>\n",
" <th>city-mpg</th>\n",
" <th>highway-mpg</th>\n",
" <th>price</th>\n",
" <th>city-L/100km</th>\n",
" <th>diesel</th>\n",
" <th>gas</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>122</td>\n",
" <td>88.6</td>\n",
" <td>0.811148</td>\n",
" <td>0.890278</td>\n",
" <td>48.8</td>\n",
" <td>2548</td>\n",
" <td>130</td>\n",
" <td>...</td>\n",
" <td>2.68</td>\n",
" <td>9.0</td>\n",
" <td>111.0</td>\n",
" <td>5000.0</td>\n",
" <td>21</td>\n",
" <td>27</td>\n",
" <td>13495.0</td>\n",
" <td>11.190476</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>122</td>\n",
" <td>88.6</td>\n",
" <td>0.811148</td>\n",
" <td>0.890278</td>\n",
" <td>48.8</td>\n",
" <td>2548</td>\n",
" <td>130</td>\n",
" <td>...</td>\n",
" <td>2.68</td>\n",
" <td>9.0</td>\n",
" <td>111.0</td>\n",
" <td>5000.0</td>\n",
" <td>21</td>\n",
" <td>27</td>\n",
" <td>16500.0</td>\n",
" <td>11.190476</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>122</td>\n",
" <td>94.5</td>\n",
" <td>0.822681</td>\n",
" <td>0.909722</td>\n",
" <td>52.4</td>\n",
" <td>2823</td>\n",
" <td>152</td>\n",
" <td>...</td>\n",
" <td>3.47</td>\n",
" <td>9.0</td>\n",
" <td>154.0</td>\n",
" <td>5000.0</td>\n",
" <td>19</td>\n",
" <td>26</td>\n",
" <td>16500.0</td>\n",
" <td>12.368421</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>164</td>\n",
" <td>99.8</td>\n",
" <td>0.848630</td>\n",
" <td>0.919444</td>\n",
" <td>54.3</td>\n",
" <td>2337</td>\n",
" <td>109</td>\n",
" <td>...</td>\n",
" <td>3.40</td>\n",
" <td>10.0</td>\n",
" <td>102.0</td>\n",
" <td>5500.0</td>\n",
" <td>24</td>\n",
" <td>30</td>\n",
" <td>13950.0</td>\n",
" <td>9.791667</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>164</td>\n",
" <td>99.4</td>\n",
" <td>0.848630</td>\n",
" <td>0.922222</td>\n",
" <td>54.3</td>\n",
" <td>2824</td>\n",
" <td>136</td>\n",
" <td>...</td>\n",
" <td>3.40</td>\n",
" <td>8.0</td>\n",
" <td>115.0</td>\n",
" <td>5500.0</td>\n",
" <td>18</td>\n",
" <td>22</td>\n",
" <td>17450.0</td>\n",
" <td>13.055556</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 21 columns</p>\n",
"</div>"
],
"text/plain": [
" Unnamed: 0 Unnamed: 0.1 symboling normalized-losses wheel-base \\\n",
"0 0 0 3 122 88.6 \n",
"1 1 1 3 122 88.6 \n",
"2 2 2 1 122 94.5 \n",
"3 3 3 2 164 99.8 \n",
"4 4 4 2 164 99.4 \n",
"\n",
" length width height curb-weight engine-size ... stroke \\\n",
"0 0.811148 0.890278 48.8 2548 130 ... 2.68 \n",
"1 0.811148 0.890278 48.8 2548 130 ... 2.68 \n",
"2 0.822681 0.909722 52.4 2823 152 ... 3.47 \n",
"3 0.848630 0.919444 54.3 2337 109 ... 3.40 \n",
"4 0.848630 0.922222 54.3 2824 136 ... 3.40 \n",
"\n",
" compression-ratio horsepower peak-rpm city-mpg highway-mpg price \\\n",
"0 9.0 111.0 5000.0 21 27 13495.0 \n",
"1 9.0 111.0 5000.0 21 27 16500.0 \n",
"2 9.0 154.0 5000.0 19 26 16500.0 \n",
"3 10.0 102.0 5500.0 24 30 13950.0 \n",
"4 8.0 115.0 5500.0 18 22 17450.0 \n",
"\n",
" city-L/100km diesel gas \n",
"0 11.190476 0 1 \n",
"1 11.190476 0 1 \n",
"2 12.368421 0 1 \n",
"3 9.791667 0 1 \n",
"4 13.055556 0 1 \n",
"\n",
"[5 rows x 21 columns]"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"\n",
"df=df._get_numeric_data()\n",
"df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" Libraries for plotting \n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"%%capture\n",
"! pip install ipywidgets"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"from ipywidgets import interact, interactive, fixed, interact_manual"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<h2>Functions for plotting</h2>\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"def DistributionPlot(RedFunction, BlueFunction, RedName, BlueName, Title):\n",
" width = 12\n",
" height = 10\n",
" plt.figure(figsize=(width, height))\n",
"\n",
" ax1 = sns.distplot(RedFunction, hist=False, color=\"r\", label=RedName)\n",
" ax2 = sns.distplot(BlueFunction, hist=False, color=\"b\", label=BlueName, ax=ax1)\n",
"\n",
" plt.title(Title)\n",
" plt.xlabel('Price (in dollars)')\n",
" plt.ylabel('Proportion of Cars')\n",
"\n",
" plt.show()\n",
" plt.close()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"def PollyPlot(xtrain, xtest, y_train, y_test, lr,poly_transform):\n",
" width = 12\n",
" height = 10\n",
" plt.figure(figsize=(width, height))\n",
" \n",
" \n",
" #training data \n",
" #testing data \n",
" # lr: linear regression object \n",
" #poly_transform: polynomial transformation object \n",
" \n",
" xmax=max([xtrain.values.max(), xtest.values.max()])\n",
"\n",
" xmin=min([xtrain.values.min(), xtest.values.min()])\n",
"\n",
" x=np.arange(xmin, xmax, 0.1)\n",
"\n",
"\n",
" plt.plot(xtrain, y_train, 'ro', label='Training Data')\n",
" plt.plot(xtest, y_test, 'go', label='Test Data')\n",
" plt.plot(x, lr.predict(poly_transform.fit_transform(x.reshape(-1, 1))), label='Predicted Function')\n",
" plt.ylim([-10000, 60000])\n",
" plt.ylabel('Price')\n",
" plt.legend()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<h1 id=\"ref1\">Part 1: Training and Testing</h1>\n",
"\n",
"<p>An important step in testing your model is to split your data into training and testing data. We will place the target data <b>price</b> in a separate dataframe <b>y</b>:</p>\n"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"y_data = df['price']\n",
"\n",
"y_data = df['price']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"drop price data in x data\n"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"x_data=df.drop('price',axis=1)\n",
"\n",
"x_data=df.drop('price',axis=1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we randomly split our data into training and testing data using the function <b>train_test_split</b>. \n"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"number of test samples : 21\n",
"number of training samples: 180\n",
"number of test samples : 21\n",
"number of training samples : 180\n"
]
}
],
"source": [
"from sklearn.model_selection import train_test_split\n",
"\n",
"\n",
"x_train, x_test, y_train, y_test = train_test_split(x_data, y_data, test_size=0.10, random_state=1)\n",
"\n",
"\n",
"print(\"number of test samples :\", x_test.shape[0])\n",
"print(\"number of training samples:\",x_train.shape[0])\n",
"\n",
"\n",
"from sklearn.model_selection import train_test_split\n",
"\n",
"x_train,x_test, y_train, y_test = train_test_split(x_data, y_data, test_size=0.10, random_state=1)\n",
"\n",
"print(\"number of test samples :\", x_test.shape[0])\n",
"print(\"number of training samples :\", x_train.shape[0])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The <b>test_size</b> parameter sets the proportion of data that is split into the testing set. In the above, the testing set is set to 10% of the total dataset. \n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<div class=\"alert alert-danger alertdanger\" style=\"margin-top: 20px\">\n",
"<h1> Question #1):</h1>\n",
"\n",
"<b>Use the function \"train_test_split\" to split up the data set such that 40% of the data samples will be utilized for testing, set the parameter \"random_state\" equal to zero. The output of the function should be the following: \"x_train_1\" , \"x_test_1\", \"y_train_1\" and \"y_test_1\".</b>\n",
"\n",
"</div>\n"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"number of test samples : 81\n",
"number of training samples : 120\n"
]
}
],
"source": [
"# Write your code below and press Shift+Enter to execute \n",
"from sklearn.model_selection import train_test_split\n",
"\n",
"x_train_1, x_test_1, y_train_1, y_test_1 = train_test_split(x_data, y_data, test_size=0.40, random_state=0)\n",
"\n",
"print(\"number of test samples :\", x_test_1.shape[0])\n",
"print(\"number of training samples :\", x_train_1.shape[0])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Double-click <b>here</b> for the solution.\n",
"\n",
"<!-- The answer is below:\n",
"\n",
"x_train1, x_test1, y_train1, y_test1 = train_test_split(x_data, y_data, test_size=0.4, random_state=0) \n",
"print(\"number of test samples :\", x_test1.shape[0])\n",
"print(\"number of training samples:\",x_train1.shape[0])\n",
"\n",
"-->\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's import <b>LinearRegression</b> from the module <b>linear_model</b>.\n"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.linear_model import LinearRegression\n",
"\n",
"from sklearn.linear_model import LinearRegression"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" We create a Linear Regression object:\n"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"lre=LinearRegression()\n",
"\n",
"lre=LinearRegression()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"we fit the model using the feature horsepower \n"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n",
" normalize=False)"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lre.fit(x_train[['horsepower']], y_train)\n",
"\n",
"lre.fit(x_train[['horsepower']], y_train)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's Calculate the R^2 on the test data:\n"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.36358755750788263"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lre.score(x_test[['horsepower']], y_test)\n",
"\n",
"lre.score(x_test[['horsepower']], y_test)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"we can see the R^2 is much smaller using the test data.\n"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.6619724197515104"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lre.score(x_train[['horsepower']], y_train)\n",
"\n",
"lre.score(x_train[['horsepower']], y_train)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<div class=\"alert alert-danger alertdanger\" style=\"margin-top: 20px\">\n",
"<h1> Question #2): </h1>\n",
"<b> \n",
"Find the R^2 on the test data using 90% of the data for training data\n",
"</b>\n",
"</div>\n"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"number of test samples : 21\n",
"number of training samples : 180\n"
]
},
{
"data": {
"text/plain": [
"0.7340722810055448"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Write your code below and press Shift+Enter to execute \n",
"\n",
"x_train_1, x_test_1, y_train_1, y_test_1 = train_test_split(x_data, y_data, test_size=0.10, random_state=0)\n",
"print(\"number of test samples :\", x_test_1.shape[0])\n",
"print(\"number of training samples :\", x_train_1.shape[0])\n",
"\n",
"lre.fit(x_train_1[['horsepower']], y_train_1)\n",
"\n",
"lre.score(x_test_1[['horsepower']], y_test_1)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Double-click <b>here</b> for the solution.\n",
"\n",
"<!-- The answer is below:\n",
"\n",
"x_train1, x_test1, y_train1, y_test1 = train_test_split(x_data, y_data, test_size=0.1, random_state=0)\n",
"lre.fit(x_train1[['horsepower']],y_train1)\n",
"lre.score(x_test1[['horsepower']],y_test1)\n",
"\n",
"-->\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" Sometimes you do not have sufficient testing data; as a result, you may want to perform Cross-validation. Let's go over several methods that you can use for Cross-validation. \n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<h2>Cross-validation Score</h2>\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Lets import <b>model_selection</b> from the module <b>cross_val_score</b>.\n"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.model_selection import cross_val_score\n",
"\n",
"from sklearn.model_selection import cross_val_score"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We input the object, the feature in this case ' horsepower', the target data (y_data). The parameter 'cv' determines the number of folds; in this case 4. \n"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"Rcross = cross_val_score(lre, x_data[['horsepower']], y_data, cv=4)\n",
"\n",
"Rcross = cross_val_score(lre, x_data[['horsepower']], y_data, cv=4)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The default scoring is R^2; each element in the array has the average R^2 value in the fold:\n"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([0.7746232 , 0.51716687, 0.74785353, 0.04839605])"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Rcross"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" We can calculate the average and standard deviation of our estimate:\n"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The mean of the folds are 0.522009915042119 and the standard deviation is 0.291183944475603\n",
"The mean of the folds are 0.522009915042119 and the standard deviation is 0.291183944475603\n"
]
}
],
"source": [
"print(\"The mean of the folds are\", Rcross.mean(), \"and the standard deviation is\" , Rcross.std())\n",
"\n",
"print(\"The mean of the folds are\", Rcross.mean(), \"and the standard deviation is\", Rcross.std())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can use negative squared error as a score by setting the parameter 'scoring' metric to 'neg_mean_squared_error'. \n"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([20254142.84026702, 43745493.2650517 , 12539630.34014931,\n",
" 17561927.72247591])"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"-1 * cross_val_score(lre,x_data[['horsepower']], y_data,cv=4,scoring='neg_mean_squared_error')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<div class=\"alert alert-danger alertdanger\" style=\"margin-top: 20px\">\n",
"<h1> Question #3): </h1>\n",
"<b> \n",
"Calculate the average R^2 using two folds, find the average R^2 for the second fold utilizing the horsepower as a feature : \n",
"</b>\n",
"</div>\n"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The mean of the folds are 0.5166761697127429\n"
]
}
],
"source": [
"# Write your code below and press Shift+Enter to execute \n",
"\n",
"Rcross = cross_val_score(lre, x_data[['horsepower']], y_data, cv=2)\n",
"Rcross\n",
"\n",
"print(\"The mean of the folds are\", Rcross.mean())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Double-click <b>here</b> for the solution.\n",
"\n",
"<!-- The answer is below:\n",
"\n",
"Rc=cross_val_score(lre,x_data[['horsepower']], y_data,cv=2)\n",
"Rc.mean()\n",
"\n",
"-->\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can also use the function 'cross_val_predict' to predict the output. The function splits up the data into the specified number of folds, using one fold for testing and the other folds are used for training. First import the function:\n"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.model_selection import cross_val_predict\n",
"\n",
"from sklearn.model_selection import cross_val_predict"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We input the object, the feature in this case <b>'horsepower'</b> , the target data <b>y_data</b>. The parameter 'cv' determines the number of folds; in this case 4. We can produce an output:\n"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([14141.63807508, 14141.63807508, 20814.29423473, 12745.03562306,\n",
" 14762.35027598])"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"yhat = cross_val_predict(lre,x_data[['horsepower']], y_data,cv=4)\n",
"yhat[0:5]\n",
"\n",
"yhat = cross_val_predict(lre, x_data[['horsepower']], y_data, cv=4)\n",
"yhat[0:5]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<h1 id=\"ref2\">Part 2: Overfitting, Underfitting and Model Selection</h1>\n",
"\n",
"<p>It turns out that the test data sometimes referred to as the out of sample data is a much better measure of how well your model performs in the real world. One reason for this is overfitting; let's go over some examples. It turns out these differences are more apparent in Multiple Linear Regression and Polynomial Regression so we will explore overfitting in that context.</p>\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's create Multiple linear regression objects and train the model using <b>'horsepower'</b>, <b>'curb-weight'</b>, <b>'engine-size'</b> and <b>'highway-mpg'</b> as features.\n"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n",
" normalize=False)"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lr = LinearRegression()\n",
"lr.fit(x_train[['horsepower', 'curb-weight', 'engine-size', 'highway-mpg']], y_train)\n",
"\n",
"lr = LinearRegression()\n",
"lr.fit(x_train[['horsepower', 'curb-weight', 'engine-size', 'highway-mpg']], y_train)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Prediction using training data:\n"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 7426.6731551 , 28323.75090803, 14213.38819709, 4052.34146983,\n",
" 34500.19124244])"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"yhat_train = lr.predict(x_train[['horsepower', 'curb-weight', 'engine-size', 'highway-mpg']])\n",
"yhat_train[0:5]\n",
"\n",
"yhat_train = lr.predict(x_train[['horsepower', 'curb-weight', 'engine-size', 'highway-mpg']])\n",
"yhat_train[0:5]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Prediction using test data: \n"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([11349.35089149, 5884.11059106, 11208.6928275 , 6641.07786278,\n",
" 15565.79920282])"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"yhat_test = lr.predict(x_test[['horsepower', 'curb-weight', 'engine-size', 'highway-mpg']])\n",
"yhat_test[0:5]\n",
"\n",
"yhat_test = lr.predict(x_test[['horsepower', 'curb-weight', 'engine-size', 'highway-mpg']])\n",
"yhat_test[0:5]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's perform some model evaluation using our training and testing data separately. First we import the seaborn and matplotlibb library for plotting.\n"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"import seaborn as sns\n",
"\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"import seaborn as sns"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's examine the distribution of the predicted values of the training data.\n"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x720 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x720 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"Title = 'Distribution Plot of Predicted Value Using Training Data vs Training Data Distribution'\n",
"DistributionPlot(y_train, yhat_train, \"Actual Values (Train)\", \"Predicted Values (Train)\", Title)\n",
"\n",
"Title = 'Distribution Plot of Predicted Value Using Training Data vs Training Data Distribution'\n",
"DistributionPlot(y_train, yhat_train, \"Actual Values (Train)\", \"Predicted Values (Train)\", Title)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Figure 1: Plot of predicted values using the training data compared to the training data. \n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"So far the model seems to be doing well in learning from the training dataset. But what happens when the model encounters new data from the testing dataset? When the model generates new values from the test data, we see the distribution of the predicted values is much different from the actual target values. \n"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x720 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x720 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"Title='Distribution Plot of Predicted Value Using Test Data vs Data Distribution of Test Data'\n",
"DistributionPlot(y_test,yhat_test,\"Actual Values (Test)\",\"Predicted Values (Test)\",Title)\n",
"\n",
"Title='Distribution Plot of Predicted Values Using Test Data vs Data Distribution of Test Data'\n",
"DistributionPlot(y_test, yhat_test, \"Actual Values (Test)\", \"Predicted Values (Test)\", Title)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Figur 2: Plot of predicted value using the test data compared to the test data. \n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<p>Comparing Figure 1 and Figure 2; it is evident the distribution of the test data in Figure 1 is much better at fitting the data. This difference in Figure 2 is apparent where the ranges are from 5000 to 15 000. This is where the distribution shape is exceptionally different. Let's see if polynomial regression also exhibits a drop in the prediction accuracy when analysing the test dataset.</p>\n"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.preprocessing import PolynomialFeatures\n",
"\n",
"from sklearn.preprocessing import PolynomialFeatures"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<h4>Overfitting</h4>\n",
"<p>Overfitting occurs when the model fits the noise, not the underlying process. Therefore when testing your model using the test-set, your model does not perform as well as it is modelling noise, not the underlying process that generated the relationship. Let's create a degree 5 polynomial model.</p>\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's use 55 percent of the data for training and the rest for testing:\n"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [],
"source": [
"x_train, x_test, y_train, y_test = train_test_split(x_data, y_data, test_size=0.45, random_state=0)\n",
"\n",
"x_train, x_test, y_train, y_test = train_test_split(x_data, y_data, test_size=0.45, random_state=0)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We will perform a degree 5 polynomial transformation on the feature <b>'horse power'</b>. \n"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"PolynomialFeatures(degree=5, include_bias=True, interaction_only=False)"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pr = PolynomialFeatures(degree=5)\n",
"x_train_pr = pr.fit_transform(x_train[['horsepower']])\n",
"x_test_pr = pr.fit_transform(x_test[['horsepower']])\n",
"pr\n",
"\n",
"pr= PolynomialFeatures(degree=5)\n",
"x_train_pr = pr.fit_transform(x_train[['horsepower']])\n",
"x_test_pr = pr.fit_transform(x_test[['horsepower']])\n",
"pr"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now let's create a linear regression model \"poly\" and train it.\n"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n",
" normalize=False)"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"poly = LinearRegression()\n",
"poly.fit(x_train_pr, y_train)\n",
"\n",
"poly = LinearRegression()\n",
"poly.fit(x_train_pr, y_train)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can see the output of our model using the method \"predict.\" then assign the values to \"yhat\".\n"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 6728.65561887, 7307.98782321, 12213.78770965, 18893.24804015,\n",
" 19995.95195136])"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"yhat = poly.predict(x_test_pr)\n",
"yhat[0:5]\n",
"\n",
"yhat = poly.predict(x_test_pr)\n",
"yhat[0:5]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's take the first five predicted values and compare it to the actual targets. \n"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Predicted values: [ 6728.65561887 7307.98782321 12213.78770965 18893.24804015]\n",
"True values: [ 6295. 10698. 13860. 13499.]\n",
"Predicted Values: [ 6728.65561887 7307.98782321 12213.78770965 18893.24804015]\n",
"True Values: [ 6295. 10698. 13860. 13499.]\n"
]
}
],
"source": [
"print(\"Predicted values:\", yhat[0:4])\n",
"print(\"True values:\", y_test[0:4].values)\n",
"\n",
"print(\"Predicted Values:\", yhat[0:4])\n",
"print(\"True Values:\", y_test[0:4].values)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We will use the function \"PollyPlot\" that we defined at the beginning of the lab to display the training data, testing data, and the predicted function.\n"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x720 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x720 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"PollyPlot(x_train[['horsepower']], x_test[['horsepower']], y_train, y_test, poly,pr)\n",
"\n",
"PollyPlot(x_train[['horsepower']], x_test[['horsepower']], y_train, y_test, poly, pr)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Figur 4 A polynomial regression model, red dots represent training data, green dots represent test data, and the blue line represents the model prediction. \n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We see that the estimated function appears to track the data but around 200 horsepower, the function begins to diverge from the data points. \n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" R^2 of the training data:\n"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.556771690212023"
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"poly.score(x_train_pr, y_train)\n",
"\n",
"poly.score(x_train_pr, y_train)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" R^2 of the test data:\n"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"-29.871340302044153"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"poly.score(x_test_pr, y_test)\n",
"\n",
"poly.score(x_test_pr, y_test)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We see the R^2 for the training data is 0.5567 while the R^2 on the test data was -29.87. The lower the R^2, the worse the model, a Negative R^2 is a sign of overfitting.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's see how the R^2 changes on the test data for different order polynomials and plot the results:\n"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(3, 0.75, 'Maximum R^2 ')"
]
},
"execution_count": 46,
"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": [
"Rsqu_test = []\n",
"\n",
"order = [1, 2, 3, 4]\n",
"for n in order:\n",
" pr = PolynomialFeatures(degree=n)\n",
" \n",
" x_train_pr = pr.fit_transform(x_train[['horsepower']])\n",
" \n",
" x_test_pr = pr.fit_transform(x_test[['horsepower']]) \n",
" \n",
" lr.fit(x_train_pr, y_train)\n",
" \n",
" Rsqu_test.append(lr.score(x_test_pr, y_test))\n",
"\n",
"plt.plot(order, Rsqu_test)\n",
"plt.xlabel('order')\n",
"plt.ylabel('R^2')\n",
"plt.title('R^2 Using Test Data')\n",
"plt.text(3, 0.75, 'Maximum R^2 ') "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We see the R^2 gradually increases until an order three polynomial is used. Then the R^2 dramatically decreases at four.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The following function will be used in the next section; please run the cell.\n"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [],
"source": [
"def f(order, test_data):\n",
" x_train, x_test, y_train, y_test = train_test_split(x_data, y_data, test_size=test_data, random_state=0)\n",
" pr = PolynomialFeatures(degree=order)\n",
" x_train_pr = pr.fit_transform(x_train[['horsepower']])\n",
" x_test_pr = pr.fit_transform(x_test[['horsepower']])\n",
" poly = LinearRegression()\n",
" poly.fit(x_train_pr,y_train)\n",
" PollyPlot(x_train[['horsepower']], x_test[['horsepower']], y_train,y_test, poly, pr)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The following interface allows you to experiment with different polynomial orders and different amounts of data. \n"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "53308b181302408999ab914cafc8a793",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"interactive(children=(IntSlider(value=3, description='order', max=6), FloatSlider(value=0.45, description='tes…"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<function __main__.f(order, test_data)>"
]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"interact(f, order=(0, 6, 1), test_data=(0.05, 0.95, 0.05))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<div class=\"alert alert-danger alertdanger\" style=\"margin-top: 20px\">\n",
"<h1> Question #4a):</h1>\n",
"\n",
"<b>We can perform polynomial transformations with more than one feature. Create a \"PolynomialFeatures\" object \"pr1\" of degree two?</b>\n",
"\n",
"</div>\n"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [],
"source": [
"pr1 = PolynomialFeatures(degree=2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Double-click <b>here</b> for the solution.\n",
"\n",
"<!-- The answer is below:\n",
"\n",
"pr1=PolynomialFeatures(degree=2)\n",
"\n",
"-->\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<div class=\"alert alert-danger alertdanger\" style=\"margin-top: 20px\">\n",
"<h1> Question #4b): </h1>\n",
"\n",
"<b> \n",
" Transform the training and testing samples for the features 'horsepower', 'curb-weight', 'engine-size' and 'highway-mpg'. Hint: use the method \"fit_transform\" \n",
"?</b>\n",
"</div>\n"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"PolynomialFeatures(degree=2, include_bias=True, interaction_only=False)"
]
},
"execution_count": 52,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x_train_pr1 = pr1.fit_transform(x_train[['horsepower', 'curb-weight', 'engine-size', 'highway-mpg']])\n",
"x_test_pr1 = pr1.fit_transform(x_test[['horsepower', 'curb-weight', 'engine-size', 'highway-mpg']])\n",
"pr1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Double-click <b>here</b> for the solution.\n",
"\n",
"<!-- The answer is below:\n",
"\n",
"x_train_pr1=pr1.fit_transform(x_train[['horsepower', 'curb-weight', 'engine-size', 'highway-mpg']])\n",
"\n",
"x_test_pr1=pr1.fit_transform(x_test[['horsepower', 'curb-weight', 'engine-size', 'highway-mpg']])\n",
"\n",
"-->\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<!-- The answer is below:\n",
"\n",
"x_train_pr1=pr.fit_transform(x_train[['horsepower', 'curb-weight', 'engine-size', 'highway-mpg']])\n",
"x_test_pr1=pr.fit_transform(x_test[['horsepower', 'curb-weight', 'engine-size', 'highway-mpg']])\n",
"\n",
"-->\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<div class=\"alert alert-danger alertdanger\" style=\"margin-top: 20px\">\n",
"<h1> Question #4c): </h1>\n",
"<b> \n",
"How many dimensions does the new feature have? Hint: use the attribute \"shape\"\n",
"</b>\n",
"</div>\n"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(110, 15)"
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"\n",
"x_train_pr1.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Double-click <b>here</b> for the solution.\n",
"\n",
"<!-- The answer is below:\n",
"\n",
" x_train_pr1.shape #There are now 15 features:\n",
"\n",
"-->\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<div class=\"alert alert-danger alertdanger\" style=\"margin-top: 20px\">\n",
"<h1> Question #4d): </h1>\n",
"\n",
"<b> \n",
"Create a linear regression model \"poly1\" and train the object using the method \"fit\" using the polynomial features?</b>\n",
"</div>\n"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n",
" normalize=False)"
]
},
"execution_count": 55,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"poly1 = LinearRegression()\n",
"poly1.fit(x_train_pr, y_train)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Double-click <b>here</b> for the solution.\n",
"\n",
"<!-- The answer is below:\n",
"\n",
"poly1=LinearRegression().fit(x_train_pr1,y_train)\n",
"\n",
"-->\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" <div class=\"alert alert-danger alertdanger\" style=\"margin-top: 20px\">\n",
"<h1> Question #4e): </h1>\n",
"<b>Use the method \"predict\" to predict an output on the polynomial features, then use the function \"DistributionPlot\" to display the distribution of the predicted output vs the test data?</b>\n",
"</div>\n"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 864x720 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"yhat_test1 = poly1.predict(x_test_pr1)\n",
"\n",
"\n",
"Title='Distribution Plot of Predicted Values Using Test Data vs Data Distribution of Test Data'\n",
"DistributionPlot(y_test, yhat_test1, \"Actual Values (Test)\", \"Predicted Values (Test)\", Title)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Double-click <b>here</b> for the solution.\n",
"\n",
"<!-- The answer is below:\n",
"\n",
"yhat_test1=poly1.predict(x_test_pr1)\n",
"Title='Distribution Plot of Predicted Value Using Test Data vs Data Distribution of Test Data'\n",
"DistributionPlot(y_test, yhat_test1, \"Actual Values (Test)\", \"Predicted Values (Test)\", Title)\n",
"\n",
"-->\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<div class=\"alert alert-danger alertdanger\" style=\"margin-top: 20px\">\n",
"<h1> Question #4f): </h1>\n",
"\n",
"<b>Using the distribution plot above, explain in words about the two regions were the predicted prices are less accurate than the actual prices</b>\n",
"\n",
"</div>\n"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {},
"outputs": [],
"source": [
"# the prices are less accurate in the following two regions:30-40K amd the 10K range\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Double-click <b>here</b> for the solution.\n",
"\n",
"<!-- The answer is below:\n",
"\n",
"The predicted value is higher than actual value for cars where the price $ 10,000 range, conversely the predicted price is lower than the price cost in the $30,000 to $40,000 range. As such the model is not as accurate in these ranges .\n",
" \n",
"-->\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<h2 id=\"ref3\">Part 3: Ridge regression</h2> \n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" In this section, we will review Ridge Regression we will see how the parameter Alfa changes the model. Just a note here our test data will be used as validation data.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" Let's perform a degree two polynomial transformation on our data. \n"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {},
"outputs": [],
"source": [
"pr=PolynomialFeatures(degree=2)\n",
"x_train_pr=pr.fit_transform(x_train[['horsepower', 'curb-weight', 'engine-size', 'highway-mpg','normalized-losses','symboling']])\n",
"x_test_pr=pr.fit_transform(x_test[['horsepower', 'curb-weight', 'engine-size', 'highway-mpg','normalized-losses','symboling']])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" Let's import <b>Ridge</b> from the module <b>linear models</b>.\n"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.linear_model import Ridge"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's create a Ridge regression object, setting the regularization parameter to 0.1 \n"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {},
"outputs": [],
"source": [
"RigeModel=Ridge(alpha=0.1)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Like regular regression, you can fit the model using the method <b>fit</b>.\n"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/jupyterlab/conda/envs/python/lib/python3.6/site-packages/sklearn/linear_model/ridge.py:125: LinAlgWarning: Ill-conditioned matrix (rcond=1.02972e-16): result may not be accurate.\n",
" overwrite_a=True).T\n"
]
},
{
"data": {
"text/plain": [
"Ridge(alpha=0.1, copy_X=True, fit_intercept=True, max_iter=None,\n",
" normalize=False, random_state=None, solver='auto', tol=0.001)"
]
},
"execution_count": 68,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"RigeModel.fit(x_train_pr, y_train)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" Similarly, you can obtain a prediction: \n"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {},
"outputs": [],
"source": [
"yhat = RigeModel.predict(x_test_pr)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's compare the first five predicted samples to our test set \n"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"predicted: [ 6567.83081933 9597.97151399 20836.22326843 19347.69543463]\n",
"test set : [ 6295. 10698. 13860. 13499.]\n"
]
}
],
"source": [
"print('predicted:', yhat[0:4])\n",
"print('test set :', y_test[0:4].values)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We select the value of Alpha that minimizes the test error, for example, we can use a for loop. \n"
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {},
"outputs": [],
"source": [
"Rsqu_test = []\n",
"Rsqu_train = []\n",
"dummy1 = []\n",
"Alpha = 10 * np.array(range(0,1000))\n",
"for alpha in Alpha:\n",
" RigeModel = Ridge(alpha=alpha) \n",
" RigeModel.fit(x_train_pr, y_train)\n",
" Rsqu_test.append(RigeModel.score(x_test_pr, y_test))\n",
" Rsqu_train.append(RigeModel.score(x_train_pr, y_train))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can plot out the value of R^2 for different Alphas \n"
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x7f7291a43f98>"
]
},
"execution_count": 72,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x720 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"width = 12\n",
"height = 10\n",
"plt.figure(figsize=(width, height))\n",
"\n",
"plt.plot(Alpha,Rsqu_test, label='validation data ')\n",
"plt.plot(Alpha,Rsqu_train, 'r', label='training Data ')\n",
"plt.xlabel('alpha')\n",
"plt.ylabel('R^2')\n",
"plt.legend()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Figure 6**:The blue line represents the R^2 of the validation data, and the red line represents the R^2 of the training data. The x-axis represents the different values of Alpha. \n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here the model is built and tested on the same data. So the training and test data are the same.\n",
"\n",
"The red line in figure 6 represents the R^2 of the test data. \n",
"As Alpha increases the R^2 decreases. \n",
"Therefore as Alpha increases the model performs worse on the test data. \n",
"\n",
"The blue line represents the R^2 on the validation data. \n",
"As the value for Alpha increases the R^2 increases and converges at a point \n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<div class=\"alert alert-danger alertdanger\" style=\"margin-top: 20px\">\n",
"<h1> Question #5): </h1>\n",
"\n",
"Perform Ridge regression and calculate the R^2 using the polynomial features, use the training data to train the model and test data to test the model. The parameter alpha should be set to 10.\n",
"\n",
"</div>\n"
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.5418576440206702"
]
},
"execution_count": 73,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Write your code below and press Shift+Enter to execute \n",
"RigeModel=Ridge(alpha=10)\n",
"RigeModel.fit(x_train_pr, y_train)\n",
"RigeModel.score(x_test_pr, y_test)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Double-click <b>here</b> for the solution.\n",
"\n",
"<!-- The answer is below:\n",
"\n",
"RigeModel = Ridge(alpha=10) \n",
"RigeModel.fit(x_train_pr, y_train)\n",
"RigeModel.score(x_test_pr, y_test)\n",
"\n",
"-->\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<h2 id=\"ref4\">Part 4: Grid Search</h2>\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The term Alfa is a hyperparameter, sklearn has the class <b>GridSearchCV</b> to make the process of finding the best hyperparameter simpler.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's import <b>GridSearchCV</b> from the module <b>model_selection</b>.\n"
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.model_selection import GridSearchCV"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We create a dictionary of parameter values:\n"
]
},
{
"cell_type": "code",
"execution_count": 75,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[{'alpha': [0.001, 0.1, 1, 10, 100, 1000, 10000, 100000, 100000]}]"
]
},
"execution_count": 75,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"parameters1= [{'alpha': [0.001,0.1,1, 10, 100, 1000, 10000, 100000, 100000]}]\n",
"parameters1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Create a ridge regions object:\n"
]
},
{
"cell_type": "code",
"execution_count": 76,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Ridge(alpha=1.0, copy_X=True, fit_intercept=True, max_iter=None,\n",
" normalize=False, random_state=None, solver='auto', tol=0.001)"
]
},
"execution_count": 76,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"RR=Ridge()\n",
"RR"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Create a ridge grid search object \n"
]
},
{
"cell_type": "code",
"execution_count": 77,
"metadata": {},
"outputs": [],
"source": [
"Grid1 = GridSearchCV(RR, parameters1,cv=4)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Fit the model \n"
]
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/jupyterlab/conda/envs/python/lib/python3.6/site-packages/sklearn/model_selection/_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
" DeprecationWarning)\n"
]
},
{
"data": {
"text/plain": [
"GridSearchCV(cv=4, error_score='raise-deprecating',\n",
" estimator=Ridge(alpha=1.0, copy_X=True, fit_intercept=True, max_iter=None,\n",
" normalize=False, random_state=None, solver='auto', tol=0.001),\n",
" fit_params=None, iid='warn', n_jobs=None,\n",
" param_grid=[{'alpha': [0.001, 0.1, 1, 10, 100, 1000, 10000, 100000, 100000]}],\n",
" pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
" scoring=None, verbose=0)"
]
},
"execution_count": 78,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Grid1.fit(x_data[['horsepower', 'curb-weight', 'engine-size', 'highway-mpg']], y_data)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The object finds the best parameter values on the validation data. We can obtain the estimator with the best parameters and assign it to the variable BestRR as follows:\n"
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Ridge(alpha=10000, copy_X=True, fit_intercept=True, max_iter=None,\n",
" normalize=False, random_state=None, solver='auto', tol=0.001)"
]
},
"execution_count": 79,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"BestRR=Grid1.best_estimator_\n",
"BestRR"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" We now test our model on the test data \n"
]
},
{
"cell_type": "code",
"execution_count": 80,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.8411649831036152"
]
},
"execution_count": 80,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"BestRR.score(x_test[['horsepower', 'curb-weight', 'engine-size', 'highway-mpg']], y_test)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<div class=\"alert alert-danger alertdanger\" style=\"margin-top: 20px\">\n",
"<h1> Question #6): </h1>\n",
"Perform a grid search for the alpha parameter and the normalization parameter, then find the best values of the parameters\n",
"</div>\n"
]
},
{
"cell_type": "code",
"execution_count": 83,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/jupyterlab/conda/envs/python/lib/python3.6/site-packages/sklearn/model_selection/_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
" DeprecationWarning)\n"
]
},
{
"data": {
"text/plain": [
"Ridge(alpha=0.1, copy_X=True, fit_intercept=True, max_iter=None,\n",
" normalize=True, random_state=None, solver='auto', tol=0.001)"
]
},
"execution_count": 83,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Write your code below and press Shift+Enter to execute \n",
"parameters2 = [{'alpha': [0.001,0.1,1,10,100,1000,10000,100000,1000000],'normalize':[True,False]}]\n",
"Grid2 = GridSearchCV(Ridge(), parameters2,cv=4)\n",
"Grid2.fit(x_data[['horsepower', 'curb-weight', 'engine-size', 'highway-mpg']], y_data)\n",
"Grid2.best_estimator_"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Double-click <b>here</b> for the solution.\n",
"\n",
"<!-- The answer is below:\n",
"\n",
"parameters2= [{'alpha': [0.001,0.1,1, 10, 100, 1000,10000,100000,100000],'normalize':[True,False]} ]\n",
"Grid2 = GridSearchCV(Ridge(), parameters2,cv=4)\n",
"Grid2.fit(x_data[['horsepower', 'curb-weight', 'engine-size', 'highway-mpg']],y_data)\n",
"Grid2.best_estimator_\n",
"\n",
"-->\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Thank you for completing this lab!\n",
"\n",
"## Author\n",
"\n",
"<a href=\"https://www.linkedin.com/in/joseph-s-50398b136/\" target=\"_blank\">Joseph Santarcangelo</a>\n",
"\n",
"### Other Contributors\n",
"\n",
"<a href=\"https://www.linkedin.com/in/mahdi-noorian-58219234/\" target=\"_blank\">Mahdi Noorian PhD</a>\n",
"\n",
"Bahare Talayian\n",
"\n",
"Eric Xiao\n",
"\n",
"Steven Dong\n",
"\n",
"Parizad\n",
"\n",
"Hima Vasudevan\n",
"\n",
"<a href=\"https://www.linkedin.com/in/fiorellawever/\" target=\"_blank\">Fiorella Wenver</a>\n",
"\n",
"<a href=\" https://www.linkedin.com/in/yi-leng-yao-84451275/ \" target=\"_blank\" >Yi Yao</a>.\n",
"\n",
"## Change Log\n",
"\n",
"| Date (YYYY-MM-DD) | Version | Changed By | Change Description |\n",
"| ----------------- | ------- | ---------- | ----------------------------------- |\n",
"| 2020-10-30 | 2.3 | Lakshmi | Changed URL of csv |\n",
"| 2020-10-05 | 2.2 | Lakshmi | Removed unused library imports |\n",
"| 2020-09-14 | 2.1 | Lakshmi | Made changes in OverFitting section |\n",
"| 2020-08-27 | 2.0 | Lavanya | Moved lab to course repo in GitLab |\n",
"\n",
"<hr>\n",
"\n",
"## <h3 align=\"center\"> © IBM Corporation 2020. All rights reserved. <h3/>\n"
]
}
],
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"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python",
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"name": "conda-env-python-py"
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