Support Vector Machines 2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "96e88771",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import datasets, svm\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7da493cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "iris = datasets.load_iris()\n",
    "x = iris.data[:, 0:2]\n",
    "y = iris.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1a322061",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "fc7e6507",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC()"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier = svm.SVC()\n",
    "classifier.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26f2ffdb",
   "metadata": {},
   "source": [
    "#### default parameters for svm :</br>\n",
    "class sklearn.svm.SVC(*, C=1.0, kernel='rbf', degree=3, gamma='scale', coef0=0.0, shrinking=True, probability=False, tol=0.001, cache_size=200, class_weight=None, verbose=False, max_iter=- 1, decision_function_shape='ovr', break_ties=False, random_state=None)</br>\n",
    "<b>comparison is one vs one</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2e83a2d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier.score(x_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d8733def",
   "metadata": {},
   "outputs": [],
   "source": [
    "def makegrid(x1, x2, h=0.01) :\n",
    "    x1_min, x1_max = x1.min()-1, x1.max()+1\n",
    "    x2_min, x2_max = x2.min()-1, x2.max()+1\n",
    "    a = np.arange(x1_min, x1_max, h)\n",
    "    b = np.arange(x2_min, x2_max, h)\n",
    "    xx, yy = np.meshgrid(a, b) # important step - makes every combination of a values with b values to make a point\n",
    "    return xx, yy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "679890fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "xx, yy = makegrid(x[:, 0], x[:, 1])\n",
    "predictions = classifier.predict(np.c_[xx.ravel(), yy.ravel()])\n",
    "plt.scatter(xx.ravel(), yy.ravel(), c=predictions)\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}