10-1_statsmodels.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lecture 10-1\n",
    "\n",
    "# Modeling in Python: statsmodels & sklearn\n",
    "\n",
    "## Week 10 Monday\n",
    "\n",
    "## Miles Chen, PhD"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# modeling data in python:\n",
    "\n",
    "Most popular package is scikit learn. Scikit-learn is not the best for a 'traditional' stats approach.\n",
    "\n",
    "the statsmodels library is better suited for traditional stats\n",
    "\n",
    "Be sure to visit and read the documentation on the StatsModels website: https://www.statsmodels.org/stable/index.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# the statsmodels.api uses numpy array notation\n",
    "# statsmodels.formula.api use formula notation (similar to R's formula notation)\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import statsmodels.api as sm\n",
    "import statsmodels.formula.api as smf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# A minimal OLS example"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Four pairs of points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.array([1,2,3,4])\n",
    "y = np.array([2,6,4,8])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x,y, marker = '.')\n",
    "plt.xlim(0,5)\n",
    "plt.ylim(0,10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   x  y\n",
      "0  1  2\n",
      "1  2  6\n",
      "2  3  4\n",
      "3  4  8\n"
     ]
    }
   ],
   "source": [
    "# make a dataframe of our data\n",
    "d = pd.DataFrame({'x':x, 'y':y})\n",
    "print(d)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Seaborn lmplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1c0e5177f10>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(x = 'x', y = 'y', data = d)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## formula notation with statsmodels\n",
    "use statsmodels.formula.api (often imported as smf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data is in a dataframe\n",
    "model = smf.ols('y ~ x', data = d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<statsmodels.regression.linear_model.OLS object at 0x000001C0E344AF10>\n"
     ]
    }
   ],
   "source": [
    "print(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# estimation of coefficients is not done until you call fit() on the model\n",
    "results = model.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<statsmodels.regression.linear_model.RegressionResultsWrapper object at 0x000001C0E3C1F210>\n"
     ]
    }
   ],
   "source": [
    "print(results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       0.640\n",
      "Model:                            OLS   Adj. R-squared:                  0.460\n",
      "Method:                 Least Squares   F-statistic:                     3.556\n",
      "Date:                Mon, 03 Jun 2024   Prob (F-statistic):              0.200\n",
      "Time:                        09:12:24   Log-Likelihood:                -6.8513\n",
      "No. Observations:                   4   AIC:                             17.70\n",
      "Df Residuals:                       2   BIC:                             16.48\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept      1.0000      2.324      0.430      0.709      -8.998      10.998\n",
      "x              1.6000      0.849      1.886      0.200      -2.051       5.251\n",
      "==============================================================================\n",
      "Omnibus:                          nan   Durbin-Watson:                   3.400\n",
      "Prob(Omnibus):                    nan   Jarque-Bera (JB):                0.308\n",
      "Skew:                          -0.000   Prob(JB):                        0.857\n",
      "Kurtosis:                       1.640   Cond. No.                         7.47\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Miles\\anaconda3\\Lib\\site-packages\\statsmodels\\stats\\stattools.py:74: ValueWarning: omni_normtest is not valid with less than 8 observations; 4 samples were given.\n",
      "  warn(\"omni_normtest is not valid with less than 8 observations; %i \"\n"
     ]
    }
   ],
   "source": [
    "print(results.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the abline_plot function for plotting the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sm.graphics.abline_plot(model_results = results)\n",
    "plt.scatter(d.x, d.y)\n",
    "\n",
    "plt.xlim(0,5)\n",
    "plt.ylim(0,10)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generating an anova table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           df  sum_sq  mean_sq         F  PR(>F)\n",
      "x         1.0    12.8     12.8  3.555556     0.2\n",
      "Residual  2.0     7.2      3.6       NaN     NaN\n"
     ]
    }
   ],
   "source": [
    "print(sm.stats.anova_lm(results))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Making predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    4.2\n",
       "1    5.8\n",
       "2    7.4\n",
       "dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.predict({'x' : np.array([2,3,4])})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## numpy array notation\n",
    "similar to sklearn's notation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 2 3 4]\n"
     ]
    }
   ],
   "source": [
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = sm.add_constant(x)  # need to add a constant for the intercept term.\n",
    "# because we are using the numpy notation, we use sm rather than smf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1. 1.]\n",
      " [1. 2.]\n",
      " [1. 3.]\n",
      " [1. 4.]]\n"
     ]
    }
   ],
   "source": [
    "print(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$y_i = \\beta_0 + \\beta_1 x_i + \\epsilon_i$$\n",
    "\n",
    "$$\\hat y_i = \\beta_0 + \\beta_1 x_i$$\n",
    "\n",
    "\n",
    "$$\\mathbf{\\hat{Y}} = \\boldsymbol{\\beta} \\mathbf{X}$$\n",
    "\n",
    "$$\\boldsymbol{\\beta} = \\left [\\beta_0, \\beta_1 \\right ]$$\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 6, 4, 8])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "model2 = sm.OLS(y, X)  # OLS is capitalized in the numpy notation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "results2 = model2.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       0.640\n",
      "Model:                            OLS   Adj. R-squared:                  0.460\n",
      "Method:                 Least Squares   F-statistic:                     3.556\n",
      "Date:                Mon, 03 Jun 2024   Prob (F-statistic):              0.200\n",
      "Time:                        09:17:58   Log-Likelihood:                -6.8513\n",
      "No. Observations:                   4   AIC:                             17.70\n",
      "Df Residuals:                       2   BIC:                             16.48\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const          1.0000      2.324      0.430      0.709      -8.998      10.998\n",
      "x1             1.6000      0.849      1.886      0.200      -2.051       5.251\n",
      "==============================================================================\n",
      "Omnibus:                          nan   Durbin-Watson:                   3.400\n",
      "Prob(Omnibus):                    nan   Jarque-Bera (JB):                0.308\n",
      "Skew:                          -0.000   Prob(JB):                        0.857\n",
      "Kurtosis:                       1.640   Cond. No.                         7.47\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Miles\\anaconda3\\Lib\\site-packages\\statsmodels\\stats\\stattools.py:74: ValueWarning: omni_normtest is not valid with less than 8 observations; 4 samples were given.\n",
      "  warn(\"omni_normtest is not valid with less than 8 observations; %i \"\n"
     ]
    }
   ],
   "source": [
    "print(results2.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "OLS solution:\n",
    "\n",
    "$$(X^TX)^{-1}X^TY$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 1.],\n",
       "       [1., 2.],\n",
       "       [1., 3.],\n",
       "       [1., 4.]])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1. , 1.6])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.linalg.inv(X.T @ X) @ (X.T @ y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example\n",
    "\n",
    "Taken directly from https://www.statsmodels.org/stable/examples/notebooks/generated/ols.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(9876789)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "generate artificial data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "nsample = 101\n",
    "x = np.linspace(0, 10, nsample)\n",
    "X = np.column_stack((x, x ** 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.  , 0.  ],\n",
       "       [0.1 , 0.01],\n",
       "       [0.2 , 0.04],\n",
       "       [0.3 , 0.09],\n",
       "       [0.4 , 0.16],\n",
       "       [0.5 , 0.25]])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[:6,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  9.5 ,  90.25],\n",
       "       [  9.6 ,  92.16],\n",
       "       [  9.7 ,  94.09],\n",
       "       [  9.8 ,  96.04],\n",
       "       [  9.9 ,  98.01],\n",
       "       [ 10.  , 100.  ]])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[95:,:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "X has two columns. First column goes from 0 to 10. Values are spaced out by 0.1.\n",
    " The second column is the first column squared. Values go from 0 to 100."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "beta = np.array([10, 5, 2])\n",
    "e = np.random.normal(size=nsample, scale = 20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = sm.add_constant(X)\n",
    "y_true = np.dot(X, beta)\n",
    "y = y_true + e"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note we added a column of 1s to the matrix X.\n",
    "\n",
    "The true values of beta are 1 for the intercept, 0.1 for the linear component, and 10 for the quadratic."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.  , 0.  , 0.  ],\n",
       "       [1.  , 0.1 , 0.01],\n",
       "       [1.  , 0.2 , 0.04],\n",
       "       [1.  , 0.3 , 0.09],\n",
       "       [1.  , 0.4 , 0.16],\n",
       "       [1.  , 0.5 , 0.25]])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[:6,:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use array notation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([10.  , 10.52, 11.08, 11.68, 12.32, 13.  ])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_true[:6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-6.81031788, 42.46990517, 20.39084082,  1.51682128, 71.00611392,\n",
       "       21.2578884 ])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y[:6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       0.924\n",
      "Model:                            OLS   Adj. R-squared:                  0.922\n",
      "Method:                 Least Squares   F-statistic:                     596.2\n",
      "Date:                Mon, 03 Jun 2024   Prob (F-statistic):           1.40e-55\n",
      "Time:                        09:24:56   Log-Likelihood:                -450.04\n",
      "No. Observations:                 101   AIC:                             906.1\n",
      "Df Residuals:                      98   BIC:                             913.9\n",
      "Df Model:                           2                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const         16.8504      6.193      2.721      0.008       4.561      29.140\n",
      "x1             2.1636      2.862      0.756      0.452      -3.516       7.843\n",
      "x2             2.2096      0.277      7.978      0.000       1.660       2.759\n",
      "==============================================================================\n",
      "Omnibus:                        1.903   Durbin-Watson:                   2.305\n",
      "Prob(Omnibus):                  0.386   Jarque-Bera (JB):                1.803\n",
      "Skew:                           0.235   Prob(JB):                        0.406\n",
      "Kurtosis:                       2.544   Cond. No.                         144.\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "model = sm.OLS(y, X)\n",
    "results = model.fit()\n",
    "print(results.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Quantities of interest can be extracted directly from the fitted model. Type `dir(results)` for a full list. Here are some examples:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters:  [16.85044423  2.16361558  2.20962084]\n",
      "R2:  0.9240498683747473\n"
     ]
    }
   ],
   "source": [
    "print(\"Parameters: \", results.params)\n",
    "print(\"R2: \", results.rsquared)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can use the `get_prediction()` method which produces a prediction results object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_ols = results.get_prediction()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<statsmodels.regression._prediction.PredictionResults at 0x1c0e5a77650>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_ols"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The prediction results object has another method `summary_frame()` which produces a data frame of predictions for each observation. The columns are as follows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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>mean</th>\n",
       "      <th>mean_se</th>\n",
       "      <th>mean_ci_lower</th>\n",
       "      <th>mean_ci_upper</th>\n",
       "      <th>obs_ci_lower</th>\n",
       "      <th>obs_ci_upper</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16.850444</td>\n",
       "      <td>6.192862</td>\n",
       "      <td>4.560911</td>\n",
       "      <td>29.139977</td>\n",
       "      <td>-26.898503</td>\n",
       "      <td>60.599391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>17.088902</td>\n",
       "      <td>5.950169</td>\n",
       "      <td>5.280985</td>\n",
       "      <td>28.896819</td>\n",
       "      <td>-26.527203</td>\n",
       "      <td>60.705007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>17.371552</td>\n",
       "      <td>5.715143</td>\n",
       "      <td>6.030038</td>\n",
       "      <td>28.713067</td>\n",
       "      <td>-26.120604</td>\n",
       "      <td>60.863709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17.698395</td>\n",
       "      <td>5.487954</td>\n",
       "      <td>6.807729</td>\n",
       "      <td>28.589061</td>\n",
       "      <td>-25.678377</td>\n",
       "      <td>61.075167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>18.069430</td>\n",
       "      <td>5.268787</td>\n",
       "      <td>7.613694</td>\n",
       "      <td>28.525166</td>\n",
       "      <td>-25.200192</td>\n",
       "      <td>61.339051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>241.259810</td>\n",
       "      <td>5.268787</td>\n",
       "      <td>230.804074</td>\n",
       "      <td>251.715546</td>\n",
       "      <td>197.990188</td>\n",
       "      <td>284.529432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>245.740740</td>\n",
       "      <td>5.487954</td>\n",
       "      <td>234.850074</td>\n",
       "      <td>256.631406</td>\n",
       "      <td>202.363968</td>\n",
       "      <td>289.117512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>250.265862</td>\n",
       "      <td>5.715143</td>\n",
       "      <td>238.924348</td>\n",
       "      <td>261.607376</td>\n",
       "      <td>206.773705</td>\n",
       "      <td>293.758019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>254.835177</td>\n",
       "      <td>5.950169</td>\n",
       "      <td>243.027260</td>\n",
       "      <td>266.643094</td>\n",
       "      <td>211.219071</td>\n",
       "      <td>298.451282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>259.448684</td>\n",
       "      <td>6.192862</td>\n",
       "      <td>247.159151</td>\n",
       "      <td>271.738217</td>\n",
       "      <td>215.699737</td>\n",
       "      <td>303.197631</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>101 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           mean   mean_se  mean_ci_lower  mean_ci_upper  obs_ci_lower  \\\n",
       "0     16.850444  6.192862       4.560911      29.139977    -26.898503   \n",
       "1     17.088902  5.950169       5.280985      28.896819    -26.527203   \n",
       "2     17.371552  5.715143       6.030038      28.713067    -26.120604   \n",
       "3     17.698395  5.487954       6.807729      28.589061    -25.678377   \n",
       "4     18.069430  5.268787       7.613694      28.525166    -25.200192   \n",
       "..          ...       ...            ...            ...           ...   \n",
       "96   241.259810  5.268787     230.804074     251.715546    197.990188   \n",
       "97   245.740740  5.487954     234.850074     256.631406    202.363968   \n",
       "98   250.265862  5.715143     238.924348     261.607376    206.773705   \n",
       "99   254.835177  5.950169     243.027260     266.643094    211.219071   \n",
       "100  259.448684  6.192862     247.159151     271.738217    215.699737   \n",
       "\n",
       "     obs_ci_upper  \n",
       "0       60.599391  \n",
       "1       60.705007  \n",
       "2       60.863709  \n",
       "3       61.075167  \n",
       "4       61.339051  \n",
       "..            ...  \n",
       "96     284.529432  \n",
       "97     289.117512  \n",
       "98     293.758019  \n",
       "99     298.451282  \n",
       "100    303.197631  \n",
       "\n",
       "[101 rows x 6 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_ols.summary_frame()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the summary frame, we can produce a prediction interval."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "iv_l = pred_ols.summary_frame()[\"obs_ci_lower\"]\n",
    "iv_u = pred_ols.summary_frame()[\"obs_ci_upper\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1c0e5ea8ed0>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 6))\n",
    "plt.plot(x, y, \"o\", label=\"data\")\n",
    "plt.plot(x, y_true, \"b\", label=\"True\")\n",
    "plt.plot(x, results.fittedvalues, \"r--\", label=\"OLS\")\n",
    "plt.plot(x, iv_u, \"r:\")\n",
    "plt.plot(x, iv_l, \"r:\")\n",
    "plt.legend(loc=\"best\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Scikit Learn\n",
    "\n",
    "## classification example with iris data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_iris"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sklearn.utils._bunch.Bunch"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris = load_iris()\n",
    "type(iris) # iris is a Bunch (like a dictionary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['data', 'target', 'frame', 'target_names', 'DESCR', 'feature_names', 'filename', 'data_module'])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[5.1 3.5 1.4 0.2]\n",
      " [4.9 3.  1.4 0.2]\n",
      " [4.7 3.2 1.3 0.2]\n",
      " [4.6 3.1 1.5 0.2]\n",
      " [5.  3.6 1.4 0.2]\n",
      " [5.4 3.9 1.7 0.4]\n",
      " [4.6 3.4 1.4 0.3]\n",
      " [5.  3.4 1.5 0.2]\n",
      " [4.4 2.9 1.4 0.2]\n",
      " [4.9 3.1 1.5 0.1]]\n"
     ]
    }
   ],
   "source": [
    "print(iris.data[:10,]) # first 10 rows: we have 4 columns of variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']\n"
     ]
    }
   ],
   "source": [
    "print(iris.feature_names)  # The predictor variables (features) are four numeric variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.target # the target is a categorical variable with three levels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['setosa' 'versicolor' 'virginica']\n"
     ]
    }
   ],
   "source": [
    "print(iris.target_names) # the observations are one of the following three species"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".. _iris_dataset:\n",
      "\n",
      "Iris plants dataset\n",
      "--------------------\n",
      "\n",
      "**Data Set Characteristics:**\n",
      "\n",
      "    :Number of Instances: 150 (50 in each of three classes)\n",
      "    :Number of Attributes: 4 numeric, predictive attributes and the class\n",
      "    :Attribute Information:\n",
      "        - sepal length in cm\n",
      "        - sepal width in cm\n",
      "        - petal length in cm\n",
      "        - petal width in cm\n",
      "        - class:\n",
      "                - Iris-Setosa\n",
      "                - Iris-Versicolour\n",
      "                - Iris-Virginica\n",
      "                \n",
      "    :Summary Statistics:\n",
      "\n",
      "    ============== ==== ==== ======= ===== ====================\n",
      "                    Min  Max   Mean    SD   Class Correlation\n",
      "    ============== ==== ==== ======= ===== ====================\n",
      "    sepal length:   4.3  7.9   5.84   0.83    0.7826\n",
      "    sepal width:    2.0  4.4   3.05   0.43   -0.4194\n",
      "    petal length:   1.0  6.9   3.76   1.76    0.9490  (high!)\n",
      "    petal width:    0.1  2.5   1.20   0.76    0.9565  (high!)\n",
      "    ============== ==== ==== ======= ===== ====================\n",
      "\n",
      "    :Missing Attribute Values: None\n",
      "    :Class Distribution: 33.3% for each of 3 classes.\n",
      "    :Creator: R.A. Fisher\n",
      "    :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\n",
      "    :Date: July, 1988\n",
      "\n",
      "The famous Iris database, first used by Sir R.A. Fisher. The dataset is taken\n",
      "from Fisher's paper. Note that it's the same as in R, but not as in the UCI\n",
      "Machine Learning Repository, which has two wrong data points.\n",
      "\n",
      "This is perhaps the best known database to be found in the\n",
      "pattern recognition literature.  Fisher's paper is a classic in the field and\n",
      "is referenced frequently to this day.  (See Duda & Hart, for example.)  The\n",
      "data set contains 3 classes of 50 instances each, where each class refers to a\n",
      "type of iris plant.  One class is linearly separable from the other 2; the\n",
      "latter are NOT linearly separable from each other.\n",
      "\n",
      ".. topic:: References\n",
      "\n",
      "   - Fisher, R.A. \"The use of multiple measurements in taxonomic problems\"\n",
      "     Annual Eugenics, 7, Part II, 179-188 (1936); also in \"Contributions to\n",
      "     Mathematical Statistics\" (John Wiley, NY, 1950).\n",
      "   - Duda, R.O., & Hart, P.E. (1973) Pattern Classification and Scene Analysis.\n",
      "     (Q327.D83) John Wiley & Sons.  ISBN 0-471-22361-1.  See page 218.\n",
      "   - Dasarathy, B.V. (1980) \"Nosing Around the Neighborhood: A New System\n",
      "     Structure and Classification Rule for Recognition in Partially Exposed\n",
      "     Environments\".  IEEE Transactions on Pattern Analysis and Machine\n",
      "     Intelligence, Vol. PAMI-2, No. 1, 67-71.\n",
      "   - Gates, G.W. (1972) \"The Reduced Nearest Neighbor Rule\".  IEEE Transactions\n",
      "     on Information Theory, May 1972, 431-433.\n",
      "   - See also: 1988 MLC Proceedings, 54-64.  Cheeseman et al\"s AUTOCLASS II\n",
      "     conceptual clustering system finds 3 classes in the data.\n",
      "   - Many, many more ...\n"
     ]
    }
   ],
   "source": [
    "print(iris.DESCR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target,\n",
    "                                                    test_size = 0.2, random_state = 1, \n",
    "                                                    stratify = iris.target)\n",
    "# we stratify on iris.target to make sure that the training and test sets have the correct "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(120, 4)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[10 10 10]\n"
     ]
    }
   ],
   "source": [
    "print(np.bincount(y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# we will fit a k nearest neighbors classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.5</td>\n",
       "      <td>2.6</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.6</td>\n",
       "      <td>2.9</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     0    1    2    3  species\n",
       "0  5.1  3.7  1.5  0.4        0\n",
       "1  4.6  3.2  1.4  0.2        0\n",
       "2  6.9  3.1  5.1  2.3        2\n",
       "3  5.5  2.6  4.4  1.2        1\n",
       "4  5.6  2.9  3.6  1.3        1"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dat = pd.DataFrame(X_train)\n",
    "dat['species'] = pd.DataFrame(y_train)\n",
    "dat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "dat = dat.replace([np.inf, -np.inf], np.nan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Miles\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "C:\\Users\\Miles\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1075: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n",
      "  data_subset = grouped_data.get_group(pd_key)\n",
      "C:\\Users\\Miles\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "C:\\Users\\Miles\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1075: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n",
      "  data_subset = grouped_data.get_group(pd_key)\n",
      "C:\\Users\\Miles\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "C:\\Users\\Miles\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1075: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n",
      "  data_subset = grouped_data.get_group(pd_key)\n",
      "C:\\Users\\Miles\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "C:\\Users\\Miles\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1075: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n",
      "  data_subset = grouped_data.get_group(pd_key)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x1c0e6787ed0>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1062.74x1000 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(dat, hue = 'species')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "knn = KNeighborsClassifier(n_neighbors=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNeighborsClassifier()\n"
     ]
    }
   ],
   "source": [
    "print(knn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "KNeighborsClassifier()"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2 0 1 0 0 0 2 2 2 1 0 1 2 1 2 0 2 1 1 2 1 1 0 0 2 1 0 0 1 1]\n"
     ]
    }
   ],
   "source": [
    "print(knn.predict(X_test))  # our predictions based on the fitted model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2 0 1 0 0 0 2 2 2 1 0 1 2 1 2 0 2 1 1 2 1 1 0 0 2 2 0 0 1 1]\n"
     ]
    }
   ],
   "source": [
    "print(y_test)  # the actual values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[10,  0,  0],\n",
       "       [ 0, 10,  0],\n",
       "       [ 0,  1,  9]], dtype=int64)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "confusion_matrix(y_test, knn.predict(X_test))\n",
    "# true answer is in rows, and the prediction is in the column\n",
    "# we see that when the true answer is 2, the predition was 1 one time.\n",
    "# no errors would be a diagonal matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[10,  0,  0],\n",
       "       [ 0, 10,  0],\n",
       "       [ 0,  1,  9]], dtype=int64)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# here we try a different knn model using only one nearest neighbor\n",
    "knn1 = KNeighborsClassifier(n_neighbors=1)\n",
    "knn1.fit(X_train, y_train)\n",
    "confusion_matrix(y_test, knn1.predict(X_test))\n",
    "# same performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[10,  0,  0],\n",
       "       [ 0, 10,  0],\n",
       "       [ 0,  2,  8]], dtype=int64)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# another knn model using 25 nearest neighbors\n",
    "knn25 = KNeighborsClassifier(n_neighbors=25)\n",
    "knn25.fit(X_train, y_train)\n",
    "confusion_matrix(y_test, knn25.predict(X_test))\n",
    "# slighlty worse performance with 25 nearest neighbors\n",
    "# we have 2 errors. The true value is 2 and we predicted 1 twice."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Automated parameter search using GridSearchCV\n",
    "\n",
    "What if I want to test out a bunch of different nearest neighbor values.\n",
    "\n",
    "So we can try a knn with 1 nearest neighbor\n",
    "\n",
    "a knn with 2 neighbors\n",
    "\n",
    "knn with 3 neighbors, etc.\n",
    "\n",
    "Rather than manually fitting 50 different models, we can have python automatically fit 50 models for us using GridSearchCV.\n",
    "\n",
    "GridSearchCV will try a bunch of different models (you can vary more than one parameter), and will report back the model score.\n",
    "\n",
    "Keep in mind that the more parameters you try out, the longer it will take to test and fit all of the models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "param_grid = {'n_neighbors': np.arange(1,50), 'weights': ['uniform','distance']}  # we make a dictionary called param_grid\n",
    "# and the keyword will be the argument that goes into the model\n",
    "\n",
    "knn = KNeighborsClassifier()  # note we do not specify n_neighbors here\n",
    "knn_cv = GridSearchCV(knn, param_grid, cv = 5)  # we call GridSearchCV, we pass it the name of our model 'knn', and \n",
    "# also we give it the parameters it will search through\n",
    "# cv=5 means we score it based on 5-fold cross validation\n",
    "\n",
    "# we are not using the test data at all\n",
    "# we are only using the training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5, estimator=KNeighborsClassifier(),\n",
       "             param_grid={&#x27;n_neighbors&#x27;: array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34,\n",
       "       35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49]),\n",
       "                         &#x27;weights&#x27;: [&#x27;uniform&#x27;, &#x27;distance&#x27;]})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5, estimator=KNeighborsClassifier(),\n",
       "             param_grid={&#x27;n_neighbors&#x27;: array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34,\n",
       "       35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49]),\n",
       "                         &#x27;weights&#x27;: [&#x27;uniform&#x27;, &#x27;distance&#x27;]})</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=5, estimator=KNeighborsClassifier(),\n",
       "             param_grid={'n_neighbors': array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34,\n",
       "       35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49]),\n",
       "                         'weights': ['uniform', 'distance']})"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn_cv.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_neighbors': 13, 'weights': 'uniform'}"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn_cv.best_params_  # which set of parameters produced the best performing model?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9833333333333334"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn_cv.best_score_   # uses an accuracy score, which may not be the best metric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[40  0  0]\n",
      " [ 0 38  2]\n",
      " [ 0  0 40]]\n"
     ]
    }
   ],
   "source": [
    "knn = KNeighborsClassifier(n_neighbors=13)\n",
    "knn.fit(X_train, y_train)\n",
    "print(confusion_matrix(y_train, knn.predict(X_train)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9833333333333333"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "118/120"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[10  0  0]\n",
      " [ 0 10  0]\n",
      " [ 0  1  9]]\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test, knn.predict(X_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.95833333 1.         1.         1.         0.95833333]\n",
      "0.9833333333333334\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "knn = KNeighborsClassifier(n_neighbors=13)\n",
    "cv_scores = cross_val_score(knn, X_train, y_train, cv = 5)\n",
    "print(cv_scores)\n",
    "print(np.mean(cv_scores))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gaussian Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "nbclass = GaussianNB()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GaussianNB()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GaussianNB</label><div class=\"sk-toggleable__content\"><pre>GaussianNB()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "GaussianNB()"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nbclass.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 0, 1, 0, 0, 0, 2, 2, 2, 1, 0, 1, 2, 1, 2, 0, 2, 1, 1, 2, 1, 1,\n",
       "       0, 0, 2, 1, 0, 0, 1, 1])"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nbclass.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[10  0  0]\n",
      " [ 0 10  0]\n",
      " [ 0  1  9]]\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test, nbclass.predict(X_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.91666667, 0.95833333, 0.95833333, 0.95833333, 0.95833333])"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_val_score(nbclass, X_train, y_train, cv = 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.95"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(cross_val_score(nbclass, X_train, y_train, cv = 5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.12.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}