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@sarchak
Created September 11, 2017 20:58
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Introduction to linear regression.\n",
"\n",
"In this jupyter notebook we will start with a very simple problem of predicting the height of the user using the weight, age and sex. \n",
"\n",
" * Simple linear model\n",
" * Linear model with non linear interactions\n",
" * Random Forest\n",
" * GridSearch to find the best parameters"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import matplotlib\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"from matplotlib import style\n",
"style.use('fivethirtyeight')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Import the data and learn about existing fields and analyze the dataframe"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"data = pd.read_csv('dataset/Howell1.csv', sep=';')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>height</th>\n",
" <th>weight</th>\n",
" <th>age</th>\n",
" <th>male</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>151.765</td>\n",
" <td>47.825606</td>\n",
" <td>63.0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>139.700</td>\n",
" <td>36.485807</td>\n",
" <td>63.0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>136.525</td>\n",
" <td>31.864838</td>\n",
" <td>65.0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>156.845</td>\n",
" <td>53.041915</td>\n",
" <td>41.0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>145.415</td>\n",
" <td>41.276872</td>\n",
" <td>51.0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" height weight age male\n",
"0 151.765 47.825606 63.0 1\n",
"1 139.700 36.485807 63.0 0\n",
"2 136.525 31.864838 65.0 0\n",
"3 156.845 53.041915 41.0 1\n",
"4 145.415 41.276872 51.0 0"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"## Visualize the data"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x110f6e9b0>"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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Cb4tWFFdbSY/VIDastToJG20oflalFxd2LGyEqNKzse5DT5qlql4fZoMVvdgt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\n1q2YO3eucB7JTcqePXuwc+dObN26FQMHDsSZM2e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"text/plain": [
"<matplotlib.figure.Figure at 0x110f6e668>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"data.plot(x='age', y='height', kind='scatter')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Fit Regression Line"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/shrikararchak/Anaconda/anaconda/envs/ds/lib/python3.5/site-packages/IPython/html.py:14: ShimWarning: The `IPython.html` package has been deprecated since IPython 4.0. You should import from `notebook` instead. `IPython.html.widgets` has moved to `ipywidgets`.\n",
" \"`IPython.html.widgets` has moved to `ipywidgets`.\", ShimWarning)\n"
]
},
{
"data": {
"text/plain": [
"<seaborn.axisgrid.FacetGrid at 0x11302d898>"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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2IRfdIAjKgrMwFAqIvotELiir0BaASsS2uGqXLPLO+75/rB83r1lSMn5JlCEK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\n4BIR5cj/A6X3zSd5jdnmAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11302d860>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import seaborn as sns\n",
"sns.lmplot(x='age',y='height',data=data,fit_reg=True) "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As you can see from the data that height of a person increase from age 0 to 20 but tend to stabilize after 20 years. During this phase we can see that its easier to fit a linear model but after that the data doesn't signify any linear interaction between age and height\n",
"\n",
"A linear model would not perform well on all the dataset. But if we consider only the data in the range age 0 to 20 we can linear model does a better job of fitting the model"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Fit a linear model for age < 20"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<seaborn.axisgrid.FacetGrid at 0x115193898>"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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YV447N9SrNul2S3ZlYVrsfKYbJLjElEPrwU2utadOuwktXd5hB1kWI49ZMyw4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\n90n4EmYpWE26jJExQRDESCZlSqHYjMzWUvaWIAg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"text/plain": [
"<matplotlib.figure.Figure at 0x11305b7b8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.lmplot(x='age',y='height',data=data[data.age < 20],fit_reg=True) "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Simple Linear model"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from sklearn.linear_model import LinearRegression\n",
"import numpy as np\n",
"from sklearn.metrics import mean_squared_error\n",
"from sklearn.model_selection import cross_val_predict"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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VV44ioVF8Ph9lZeWH9wd2DJm2IKWhoYH77ruPW265BYA33niDefPmccUVV1Bb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\nI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"text/plain": [
"<matplotlib.figure.Figure at 0x1064b5cf8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"MEAN Squared Error : 89.62222301721019. (Lower the better)\n"
]
}
],
"source": [
"lr = LinearRegression()\n",
"train = data.loc[:, data.columns != 'height']\n",
"target = data.height\n",
"# cross_val_predict returns an array of the same size as `y` where each entry\n",
"# is a prediction obtained by cross validation:\n",
"predicted = cross_val_predict(lr, train, target, cv=10)\n",
"\n",
"fig, ax = plt.subplots()\n",
"ax.scatter(target, predicted, edgecolors=(0, 0, 0))\n",
"ax.plot([target.min(), target.max()], [target.min(), target.max()], 'k--', lw=4)\n",
"ax.set_xlabel('Measured')\n",
"ax.set_ylabel('Predicted')\n",
"plt.show()\n",
"error = mean_squared_error(target, predicted)\n",
"print(\"MEAN Squared Error : {}. (Lower the better)\".format(error))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As we can see above the Error is significantly high. Predictions are off quite a bit.\n",
"\n",
"Lets try to help the linear model by adding more features.\n",
"\n",
" * As we see from the data we can probably add a new feature like age < 20"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"data['age_less_than_20'] = (data.age<20).astype(int)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>height</th>\n",
" <th>weight</th>\n",
" <th>age</th>\n",
" <th>male</th>\n",
" <th>age_less_than_20</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>151.765</td>\n",
" <td>47.825606</td>\n",
" <td>63.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>139.700</td>\n",
" <td>36.485807</td>\n",
" <td>63.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>136.525</td>\n",
" <td>31.864838</td>\n",
" <td>65.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>156.845</td>\n",
" <td>53.041915</td>\n",
" <td>41.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>145.415</td>\n",
" <td>41.276872</td>\n",
" <td>51.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" height weight age male age_less_than_20\n",
"0 151.765 47.825606 63.0 1 0\n",
"1 139.700 36.485807 63.0 0 0\n",
"2 136.525 31.864838 65.0 0 0\n",
"3 156.845 53.041915 41.0 1 0\n",
"4 145.415 41.276872 51.0 0 0"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now lets try to fit the model again with this new feature"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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Lxr+xEEKICRm5FWBUeqgrNx21rYCjuaVUUFCILuIa9fydZpcSn8837GdyM4QF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txu/YUXhiE9fRX+/OdgBaYjIiLqWVhSHqqtrUVQUABu3rzRbq1v375YuHAxoqNjOrwuChERET15\n/MPZh5ydnTF69Jh2x/X6UBQXl2H+/DgWFCIiIgtiSWllxYp09O3bFwDwhz88jx07PsO2bTswdKiL\nwpMRERH1PDzd08rQoS5YsiQJDQ13MHdurLmwEBERkeWxpLQxe3aM0iMQEREReLqHiIiIVIolhYiI\niFSJJYWIiIhUiSWFiIiIVIklhYiIiFSJJYWIiIhUiSWFiIiIVIklhYiIiFSJJYWIiIhUiSWFiIiI\nVIklhYiIiFSJJYWIiIhUSSOEEEoPQURERNQWd1KIiIhIlVhSiIiISJVYUoiIiEiVWFKIiIhIlVhS\niIiISJVYUoiIiEiVWFK6yaZNmxAREYGwsDB89tlnuHTpEqZMmYKoqCgsW7YMsiwrPaJiWlpaEBcX\nh8jISERFRaGyspL5tHLixAkYDAYA+H9zWb9+PcLDwxEZGYmTJ08qOa4iWmd07tw5REVFwWAwYMaM\nGbh58yYAYNeuXQgLC8PkyZNx+PBhJcdVROuMfpWXl4eIiAjzY2b0r4xu3bqF2bNnY+rUqYiMjMTl\ny5cB9OyM2n6dTZ48GVOmTMG7775r/l7U7fkIeuJKSkrErFmzhCRJorGxUaxdu1bMmjVLlJSUCCGE\nSEpKEvv371d4SuUcOHBAzJs3TwghRHFxsYiJiWE+D23evFmEhoaK119/XQghOszl9OnTwmAwCFmW\nRXV1tQgLC1NyZItrm9HUqVPF2bNnhRBC7Ny5U6SlpYna2loRGhoqTCaTuHPnjvntnqJtRkIIcfbs\nWTFt2jTzMWb0aEbx8fFi7969QgghvvvuO3H48OEenVHbfN5++23x9ddfCyGEWLhwoTh48KBF8uFO\nSjcoLi6Gh4cH5syZg+joaAQFBeHMmTPw8/MDAIwfPx5HjhxReErluLq6QpIkyLKMxsZG2NjYMJ+H\nXFxcsG7dOvPjjnIpLy9HYGAgNBoNnnvuOUiShLq6OqVGtri2GWVlZWH48OEAAEmSYGtri5MnT8Lb\n2xu9e/eGg4MDXFxc8MMPPyg1ssW1zai+vh6ZmZlISEgwH2NGj2Z07Ngx1NTUYPr06cjLy4Ofn1+P\nzqhtPsOHD8ft27chhEBTUxNsbGwskg9LSjeor6/H6dOnsWbNGrz33nt45513IISARqMBANjZ2aGh\noUHhKZXzzDPPoLq6Gnq9HklJSTAYDMznoQkTJsDGxsb8uKNcGhsbYW9vb35OT8urbUbOzs4AHvyQ\n2b59O6ZPn47GxkY4ODiYn2NnZ4fGxkaLz6qU1hlJkoTExEQkJCTAzs7O/Bxm9OjrqLq6GlqtFlu3\nbsWgQYPwySef9OiM2ubz/PPPw2g0Qq/X49atWxgzZoxF8rHp/Cn0Wzk6OsLNzQ29e/eGm5sbbG1t\ncf36dfN6U1MTtFqtghMqa+vWrQgMDERcXByuXbuGN954Ay0tLeb1np5Pa1ZW//o94tdc7O3t0dTU\n9Mjx1t8oeqL8/Hx8/PHH2Lx5M5ycnJhRK2fOnMGlS5ewfPlymEwmXLhwAUajEf7+/syoFUdHR4SE\nhAAAQkJC8OGHH2LkyJHM6CGj0YicnBy4u7sjJycH6enpCAwM7PZ8uJPSDXx8fFBUVAQhBGpqanD3\n7l0EBASgtLQUAFBYWAhfX1+Fp1SOVqs1v5B1Oh3u37+Pl156ifl0oKNcRo0aheLiYsiyjKtXr0KW\nZTg5OSk8qXK+/PJLbN++HdnZ2Rg6dCgAwMvLC+Xl5TCZTGhoaEBlZSU8PDwUnlQZXl5e2Lt3L7Kz\ns5GVlYVhw4YhMTGRGbXh4+ODb775BgBQVlaGYcOGMaNWdDqdeQfX2dkZd+7csUg+3EnpBsHBwSgr\nK0N4eDiEEEhOTsaQIUOQlJSErKwsuLm5YcKECUqPqZjp06cjISEBUVFRaGlpQWxsLEaOHMl8OhAf\nH98uF2tra/j6+iIiIgKyLCM5OVnpMRUjSRKMRiMGDRqEuXPnAgBGjx6NefPmwWAwICoqCkIIxMbG\nwtbWVuFp1WXAgAHMqJX4+HgsXboUubm5sLe3x6pVq6DT6ZjRQytWrEBsbCxsbGzQq1cvpKamWuQ1\nxLsgExERkSrxdA8RERGpEksKERERqRJLChEREakSSwoRERGpEksKERERqRJLChH9x65cuQJPT892\n/wZ97tw5eHp6Ys+ePQpN9ngGg8F8/RkiUi+WFCLqEkdHRxQVFUGSJPOx/Pz8Hn2BOSJ6MngxNyLq\nEjs7O7z44osoKyuDv78/AODbb7/F2LFjATy4Uu7atWtx//59DBkyBKmpqejXrx8KCgqwZcsW3Lt3\nD83NzUhLS8OoUaOwZcsWfPHFF7CysoKXlxdSUlKwZ88eHD16FOnp6QAe7ITExMQAADIyMiDLMtzd\n3ZGcnIyUlBRUVFRAkiTMnDkToaGhaG5uRmJiIk6fPo3Bgwejvr5embCI6DdhSSGiLtPr9di3bx/8\n/f1x8uRJeHp6QgiBuro6bNu2DZ9++il0Oh1yc3ORmZmJ1NRU5ObmYuPGjXBycsLnn3+OzZs3Y8OG\nDdi0aROKiopgbW2NxMRE1NTUPPZzV1VV4fDhw3BwcEBmZiZGjBiBDz74AI2NjYiMjMTLL7+M/fv3\nAwAKCgpQVVWFSZMmWSIWIuoilhQi6rKQkBCsXr0asiyjoKAAer0e+fn56NOnD65du4Zp06YBAGRZ\nhk6ng5WVFTZs2IBDhw7h4sWLOHr0KKysrGBtbQ1vb2+Eh4fjlVdewZtvvomBAwc+9nO7urqa7wV1\n5MgR3Lt3D7t37wYA/PLLL6ioqMDRo0cREREB4MHdXL29vbsxDSJ6UlhSiKjLfj3lU15ejpKSEsTF\nxSE/Px+SJGHUqFHYuHEjAMBkMqGpqQlNTU0IDw/HpEmTMHr0aHh6eiInJwcA8NFHH+H48eMoLCzE\nW2+9hczMTGg0GrS+g0fru2b36dPH/LYsy8jIyMCIESMAADdv3oROp8OuXbseef/Wt6AnIvXiH84S\n0ROh1+uxatUqjBw50lwCTCYTjh8/josXLwJ4UEBWrlyJqqoqaDQaREdHY8yYMThw4AAkSUJdXR1e\nffVVeHh4YP78+Rg3bhzOnz+Pfv36obKyEkII/PTTTzh//nyHM/j7+2Pnzp0AgNraWkyaNAnXrl1D\nQEAA8vLyIMsyqqurcezYMcuEQkRdwl8niOiJCA4ORmJiIubPn28+1r9/f6SlpWHBggWQZRkDBw5E\nRkYGtFothg8fDr1eD41Gg8DAQJSXl8PJyQkREREIDw9H37594erqitdeew02NjbYvXs3Jk6cCFdX\nV/j4+HQ4Q0xMDJYvX47Q0FBIkoRFixbBxcUFUVFRqKiogF6vx+DBg5/47eSJqHvwLshERESkSjzd\nQ0RERKrEkkJERESqxJJCREREqsSSQkRERKrEkkJERESqxJJCREREqsSSQkRERKrEkkJERESq9H/X\nUOStVPWunQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x110f34160>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"MEAN Squared Error : 85.68689844692364. (Lower the better)\n"
]
}
],
"source": [
"lr = LinearRegression()\n",
"train = data.loc[:, data.columns != 'height']\n",
"target = data.height\n",
"# cross_val_predict returns an array of the same size as `y` where each entry\n",
"# is a prediction obtained by cross validation:\n",
"predicted = cross_val_predict(lr, train, target, cv=10)\n",
"\n",
"fig, ax = plt.subplots()\n",
"ax.scatter(target, predicted, edgecolors=(0, 0, 0))\n",
"ax.plot([target.min(), target.max()], [target.min(), target.max()], 'k--', lw=4)\n",
"ax.set_xlabel('Measured')\n",
"ax.set_ylabel('Predicted')\n",
"plt.show()\n",
"error = mean_squared_error(target, predicted)\n",
"print(\"MEAN Squared Error : {}. (Lower the better)\".format(error))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can see that the error decreased, but not a lot. Let's go ahead and add a feature which is square of the age and square of the weight"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"data['squared_age'] = data['age'] ** 2"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"data['squared_weight'] = data['weight'] ** 2"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>height</th>\n",
" <th>weight</th>\n",
" <th>age</th>\n",
" <th>male</th>\n",
" <th>age_less_than_20</th>\n",
" <th>squared_age</th>\n",
" <th>squared_weight</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>151.765</td>\n",
" <td>47.825606</td>\n",
" <td>63.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>3969.0</td>\n",
" <td>2287.288637</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>139.700</td>\n",
" <td>36.485807</td>\n",
" <td>63.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3969.0</td>\n",
" <td>1331.214076</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>136.525</td>\n",
" <td>31.864838</td>\n",
" <td>65.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>4225.0</td>\n",
" <td>1015.367901</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>156.845</td>\n",
" <td>53.041915</td>\n",
" <td>41.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1681.0</td>\n",
" <td>2813.444694</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>145.415</td>\n",
" <td>41.276872</td>\n",
" <td>51.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2601.0</td>\n",
" <td>1703.780162</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" height weight age male age_less_than_20 squared_age \\\n",
"0 151.765 47.825606 63.0 1 0 3969.0 \n",
"1 139.700 36.485807 63.0 0 0 3969.0 \n",
"2 136.525 31.864838 65.0 0 0 4225.0 \n",
"3 156.845 53.041915 41.0 1 0 1681.0 \n",
"4 145.415 41.276872 51.0 0 0 2601.0 \n",
"\n",
" squared_weight \n",
"0 2287.288637 \n",
"1 1331.214076 \n",
"2 1015.367901 \n",
"3 2813.444694 \n",
"4 1703.780162 "
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.head()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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5TP0nf6Kg4FvxhMyamn3c9MCfsVUkV4v1uVoomLSAYMBDzrjZ5IybTdOWv+Pa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\nEQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1162c5320>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"MEAN Squared Error : 28.509278178481857. (Lower the better)\n"
]
}
],
"source": [
"lr = LinearRegression()\n",
"train = data.loc[:, data.columns != 'height']\n",
"target = data.height\n",
"# cross_val_predict returns an array of the same size as `y` where each entry\n",
"# is a prediction obtained by cross validation:\n",
"predicted = cross_val_predict(lr, train, target, cv=10)\n",
"\n",
"fig, ax = plt.subplots()\n",
"ax.scatter(target, predicted, edgecolors=(0, 0, 0))\n",
"ax.plot([target.min(), target.max()], [target.min(), target.max()], 'k--', lw=4)\n",
"ax.set_xlabel('Measured')\n",
"ax.set_ylabel('Predicted')\n",
"plt.show()\n",
"error = mean_squared_error(target, predicted)\n",
"print(\"MEAN Squared Error : {}. (Lower the better)\".format(error))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Great! We were able to reduce the error from 89 to 28.5 by adding higher order features. One thing to note is that the model complexity increase as we add more higher order features"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Random forest\n",
"\n",
"We can use a random forest classifier which can fit the data without even needing any higher order features"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"data = pd.read_csv('dataset/Howell1.csv', sep=';')"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>height</th>\n",
" <th>weight</th>\n",
" <th>age</th>\n",
" <th>male</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>151.765</td>\n",
" <td>47.825606</td>\n",
" <td>63.0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>139.700</td>\n",
" <td>36.485807</td>\n",
" <td>63.0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>136.525</td>\n",
" <td>31.864838</td>\n",
" <td>65.0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>156.845</td>\n",
" <td>53.041915</td>\n",
" <td>41.0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>145.415</td>\n",
" <td>41.276872</td>\n",
" <td>51.0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" height weight age male\n",
"0 151.765 47.825606 63.0 1\n",
"1 139.700 36.485807 63.0 0\n",
"2 136.525 31.864838 65.0 0\n",
"3 156.845 53.041915 41.0 1\n",
"4 145.415 41.276872 51.0 0"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.head()"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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K0pKGRWYSpAghhDgogUCA//rhz+mLZGHIKmLgz2vIVrtZ8eub0jrGdjVtAKCw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\nAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1163c1fd0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"MEAN Squared Error : 24.16933595036987. (Lower the better)\n"
]
}
],
"source": [
"from sklearn.ensemble import RandomForestRegressor\n",
"lr = RandomForestRegressor()\n",
"train = data.loc[:, data.columns != 'height']\n",
"target = data.height\n",
"# cross_val_predict returns an array of the same size as `y` where each entry\n",
"# is a prediction obtained by cross validation:\n",
"predicted = cross_val_predict(lr, train, target, cv=10)\n",
"\n",
"fig, ax = plt.subplots()\n",
"ax.scatter(target, predicted, edgecolors=(0, 0, 0))\n",
"ax.plot([target.min(), target.max()], [target.min(), target.max()], 'k--', lw=4)\n",
"ax.set_xlabel('Measured')\n",
"ax.set_ylabel('Predicted')\n",
"plt.show()\n",
"error = mean_squared_error(target, predicted)\n",
"print(\"MEAN Squared Error : {}. (Lower the better)\".format(error))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The lowest mean squared error without using any higher order features"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"params = {\n",
" 'n_estimators': [3, 5, 10, 20, 50], \n",
" 'max_depth': [3, 5, 7, 9],\n",
" 'min_samples_leaf' : [1, 2, 3, 4, 5]\n",
"}\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Use grid_search_cv to find the best parameters"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/shrikararchak/Anaconda/anaconda/envs/ds/lib/python3.5/site-packages/sklearn/cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
" \"This module will be removed in 0.20.\", DeprecationWarning)\n",
"/Users/shrikararchak/Anaconda/anaconda/envs/ds/lib/python3.5/site-packages/sklearn/grid_search.py:42: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. This module will be removed in 0.20.\n",
" DeprecationWarning)\n"
]
}
],
"source": [
"from sklearn.grid_search import GridSearchCV\n",
"from sklearn.model_selection import StratifiedKFold, KFold"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"grid = GridSearchCV(estimator=RandomForestRegressor(), param_grid=params, cv=5, verbose=1)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Fitting 5 folds for each of 100 candidates, totalling 500 fits\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[Parallel(n_jobs=1)]: Done 500 out of 500 | elapsed: 10.2s finished\n"
]
},
{
"data": {
"text/plain": [
"GridSearchCV(cv=5, error_score='raise',\n",
" estimator=RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
" max_features='auto', max_leaf_nodes=None,\n",
" min_impurity_decrease=0.0, min_impurity_split=None,\n",
" min_samples_leaf=1, min_samples_split=2,\n",
" min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,\n",
" oob_score=False, random_state=None, verbose=0, warm_start=False),\n",
" fit_params={}, iid=True, n_jobs=1,\n",
" param_grid={'max_depth': [3, 5, 7, 9], 'min_samples_leaf': [1, 2, 3, 4, 5], 'n_estimators': [3, 5, 10, 20, 50]},\n",
" pre_dispatch='2*n_jobs', refit=True, scoring=None, verbose=1)"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"grid.fit(train, target)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=5,\n",
" max_features='auto', max_leaf_nodes=None,\n",
" min_impurity_decrease=0.0, min_impurity_split=None,\n",
" min_samples_leaf=4, min_samples_split=2,\n",
" min_weight_fraction_leaf=0.0, n_estimators=50, n_jobs=1,\n",
" oob_score=False, random_state=None, verbose=0, warm_start=False)"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"grid.best_estimator_"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'max_depth': 5, 'min_samples_leaf': 4, 'n_estimators': 50}"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"grid.best_params_"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.9636125431925439"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"grid.best_score_"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Use the best params from the grid search above"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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3OvZvTnnfllefoPyEC3Af3IEhpxCvN0IsAfnTTkGjM6SMydmwiaLqxWh0JqIh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\ni9sFr5WPa8O6EtVlCCh2WrSMx5Sbxuk/NnBo6yfOAAXgxO9rXZ6l0fm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"text/plain": [
"<matplotlib.figure.Figure at 0x1157522b0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"MEAN Squared Error : 20.319800638178567. (Lower the better)\n",
"Deviation : 4.507748954653372. (Lower the better)\n"
]
}
],
"source": [
"lr = RandomForestRegressor(n_estimators=20, min_samples_leaf=2, max_depth=5)\n",
"train = data.loc[:, data.columns != 'height']\n",
"target = data.height\n",
"# cross_val_predict returns an array of the same size as `y` where each entry\n",
"# is a prediction obtained by cross validation:\n",
"predicted = cross_val_predict(lr, train, target, cv=10)\n",
"\n",
"fig, ax = plt.subplots()\n",
"ax.scatter(target, predicted, edgecolors=(0, 0, 0))\n",
"ax.plot([target.min(), target.max()], [target.min(), target.max()], 'k--', lw=4)\n",
"ax.set_xlabel('Measured')\n",
"ax.set_ylabel('Predicted')\n",
"plt.show()\n",
"error = mean_squared_error(target, predicted)\n",
"deviation = np.sqrt(error)\n",
"print(\"MEAN Squared Error : {}. (Lower the better)\".format(error))\n",
"print(\"Deviation : {}. (Lower the better)\".format(deviation))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"With random forest we are able to get the lowest mean squared error and able to predict the height within +- 4.47 cms of the true value ( 4.47 = np.sqrt(19.7))"
]
}
],
"metadata": {
"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": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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