naive-bayes teste.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "increasing-psychiatry",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of mislabeled points out of a total 75 points : 4\n"
     ]
    }
   ],
   "source": [
    "from sklearn.datasets import load_iris\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "X, y = load_iris(return_X_y=True)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5, random_state=0)\n",
    "gnb = GaussianNB()\n",
    "y_pred = gnb.fit(X_train, y_train).predict(X_test)\n",
    "print(\"Number of mislabeled points out of a total %d points : %d\"% (X_test.shape[0], (y_test != y_pred).sum()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "searching-america",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "%matplotlib inline\n",
    "\n",
    "# graficos\n",
    "plt.figure(figsize = (20, 12))\n",
    "for i in range(len(X.T)):\n",
    "    plt.hist(X.T[i],bins=50)\n",
    "plt.figure(figsize = (20, 12))\n",
    "plt.hist(y,bins=50)\n",
    "\n",
    "plt.figure(figsize = (20, 12))\n",
    "for i in range(len(X.T)):\n",
    "    plt.hist(X_test.T[i],bins=50)\n",
    "plt.figure(figsize = (20, 12))\n",
    "plt.hist(y_pred,bins=50)\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "hungarian-designer",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}