13.5.1.서포트벡터머신 (분류 모델).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "65e7c7a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 필요한 패키지 설치\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "import sklearn.svm as svm\n",
    "from sklearn.svm import SVC\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "826ceb25",
   "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>Area</th>\n",
       "      <th>MajorAxisLength</th>\n",
       "      <th>MinorAxisLength</th>\n",
       "      <th>Eccentricity</th>\n",
       "      <th>ConvexArea</th>\n",
       "      <th>Extent</th>\n",
       "      <th>Perimeter</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>87524</td>\n",
       "      <td>442.246011</td>\n",
       "      <td>253.291155</td>\n",
       "      <td>0.819738</td>\n",
       "      <td>90546</td>\n",
       "      <td>0.758651</td>\n",
       "      <td>1184.040</td>\n",
       "      <td>Kecimen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>75166</td>\n",
       "      <td>406.690687</td>\n",
       "      <td>243.032436</td>\n",
       "      <td>0.801805</td>\n",
       "      <td>78789</td>\n",
       "      <td>0.684130</td>\n",
       "      <td>1121.786</td>\n",
       "      <td>Kecimen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>90856</td>\n",
       "      <td>442.267048</td>\n",
       "      <td>266.328318</td>\n",
       "      <td>0.798354</td>\n",
       "      <td>93717</td>\n",
       "      <td>0.637613</td>\n",
       "      <td>1208.575</td>\n",
       "      <td>Kecimen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>45928</td>\n",
       "      <td>286.540559</td>\n",
       "      <td>208.760042</td>\n",
       "      <td>0.684989</td>\n",
       "      <td>47336</td>\n",
       "      <td>0.699599</td>\n",
       "      <td>844.162</td>\n",
       "      <td>Kecimen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>79408</td>\n",
       "      <td>352.190770</td>\n",
       "      <td>290.827533</td>\n",
       "      <td>0.564011</td>\n",
       "      <td>81463</td>\n",
       "      <td>0.792772</td>\n",
       "      <td>1073.251</td>\n",
       "      <td>Kecimen</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Area  MajorAxisLength  MinorAxisLength  Eccentricity  ConvexArea  \\\n",
       "0  87524       442.246011       253.291155      0.819738       90546   \n",
       "1  75166       406.690687       243.032436      0.801805       78789   \n",
       "2  90856       442.267048       266.328318      0.798354       93717   \n",
       "3  45928       286.540559       208.760042      0.684989       47336   \n",
       "4  79408       352.190770       290.827533      0.564011       81463   \n",
       "\n",
       "     Extent  Perimeter    Class  \n",
       "0  0.758651   1184.040  Kecimen  \n",
       "1  0.684130   1121.786  Kecimen  \n",
       "2  0.637613   1208.575  Kecimen  \n",
       "3  0.699599    844.162  Kecimen  \n",
       "4  0.792772   1073.251  Kecimen  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 데이터 불러오기\n",
    "# https://www.kaggle.com/datasets/muratkokludataset/raisin-dataset\n",
    "df = pd.read_excel(\"datasets/Raisin_Dataset.xlsx\")\n",
    "\n",
    "# 데이터 샘플 확인\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0cabb47c",
   "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>MajorAxisLength</th>\n",
       "      <th>MinorAxisLength</th>\n",
       "      <th>Eccentricity</th>\n",
       "      <th>ConvexArea</th>\n",
       "      <th>Extent</th>\n",
       "      <th>Perimeter</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.280714</td>\n",
       "      <td>0.314376</td>\n",
       "      <td>0.767872</td>\n",
       "      <td>0.255504</td>\n",
       "      <td>0.831422</td>\n",
       "      <td>0.271791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.234638</td>\n",
       "      <td>0.284945</td>\n",
       "      <td>0.738636</td>\n",
       "      <td>0.208864</td>\n",
       "      <td>0.667854</td>\n",
       "      <td>0.241842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.280741</td>\n",
       "      <td>0.351778</td>\n",
       "      <td>0.733009</td>\n",
       "      <td>0.268084</td>\n",
       "      <td>0.565754</td>\n",
       "      <td>0.283594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.078935</td>\n",
       "      <td>0.186620</td>\n",
       "      <td>0.548194</td>\n",
       "      <td>0.084089</td>\n",
       "      <td>0.701809</td>\n",
       "      <td>0.108284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.164011</td>\n",
       "      <td>0.422064</td>\n",
       "      <td>0.350968</td>\n",
       "      <td>0.219472</td>\n",
       "      <td>0.906315</td>\n",
       "      <td>0.218493</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   MajorAxisLength  MinorAxisLength  Eccentricity  ConvexArea    Extent  \\\n",
       "0         0.280714         0.314376      0.767872    0.255504  0.831422   \n",
       "1         0.234638         0.284945      0.738636    0.208864  0.667854   \n",
       "2         0.280741         0.351778      0.733009    0.268084  0.565754   \n",
       "3         0.078935         0.186620      0.548194    0.084089  0.701809   \n",
       "4         0.164011         0.422064      0.350968    0.219472  0.906315   \n",
       "\n",
       "   Perimeter  \n",
       "0   0.271791  \n",
       "1   0.241842  \n",
       "2   0.283594  \n",
       "3   0.108284  \n",
       "4   0.218493  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 독립변수 정규화\n",
    "\n",
    "# 독립변수, 종속변수 분리\n",
    "df_x = df.drop(['Area','Class'],axis=1)\n",
    "df_y = df[['Class']]\n",
    "\n",
    "# 데이터 정규화 적용\n",
    "MinMaxScaler = MinMaxScaler()\n",
    "df_minmax = MinMaxScaler.fit_transform(df_x)\n",
    "\n",
    "# 컬럼명 결합\n",
    "df_x = pd.DataFrame(data=df_minmax, columns=df_x.columns)\n",
    "\n",
    "df_x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9dda0bc4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train data 개수:  540\n",
      "test data 개수:  360\n"
     ]
    }
   ],
   "source": [
    "# 학습셋과 테스트셋 분리하여 생성(6:4)\n",
    "\n",
    "x_train, x_test, y_train, y_test = train_test_split(\n",
    "    df_x,df_y,test_size=0.4,random_state=10)\n",
    "\n",
    "# 학습셋과 검증셋이 잘 나뉘었는지 확인\n",
    "print('train data 개수: ', len(x_train))\n",
    "print('test data 개수: ', len(x_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a9856bf2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8648148148148148\n",
      "0.8638888888888889\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:993: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    }
   ],
   "source": [
    "# 선형 SVM 모델 성능 확인\n",
    "\n",
    "#3차항(degree), 학습 반복횟수(max_iter) 10000 설정\n",
    "svm_model = SVC(kernel = 'linear', degree = 3, gamma = 'auto', C=10, max_iter = 1000)\n",
    "\n",
    "svm_model.fit(x_train,y_train)\n",
    "\n",
    "print(svm_model.score(x_train,y_train))\n",
    "print(svm_model.score(x_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "926ba9b5",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:993: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:993: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:993: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:993: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:993: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:993: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:993: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rbf SVM : C:1, train set score:0.857407, test set score:0.883333\n",
      "rbf SVM : C:2, train set score:0.864815, test set score:0.880556\n",
      "rbf SVM : C:3, train set score:0.862963, test set score:0.886111\n",
      "rbf SVM : C:4, train set score:0.861111, test set score:0.880556\n",
      "rbf SVM : C:5, train set score:0.861111, test set score:0.880556\n",
      "rbf SVM : C:6, train set score:0.862963, test set score:0.883333\n",
      "rbf SVM : C:7, train set score:0.866667, test set score:0.883333\n",
      "rbf SVM : C:8, train set score:0.864815, test set score:0.883333\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:993: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:993: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:993: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:993: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:993: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:993: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:993: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rbf SVM : C:9, train set score:0.864815, test set score:0.883333\n",
      "rbf SVM : C:10, train set score:0.864815, test set score:0.891667\n",
      "rbf SVM : C:11, train set score:0.864815, test set score:0.891667\n",
      "rbf SVM : C:12, train set score:0.862963, test set score:0.891667\n",
      "rbf SVM : C:13, train set score:0.864815, test set score:0.891667\n",
      "rbf SVM : C:14, train set score:0.864815, test set score:0.891667\n",
      "rbf SVM : C:15, train set score:0.864815, test set score:0.897222\n",
      "rbf SVM : C:16, train set score:0.864815, test set score:0.891667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:993: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:993: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning: Solver terminated early (max_iter=1000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  warnings.warn(\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:993: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:993: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning: Solver terminated early (max_iter=1000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  warnings.warn(\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:993: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning: Solver terminated early (max_iter=1000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  warnings.warn(\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:993: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning: Solver terminated early (max_iter=1000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  warnings.warn(\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:993: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning: Solver terminated early (max_iter=1000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rbf SVM : C:17, train set score:0.864815, test set score:0.891667\n",
      "rbf SVM : C:18, train set score:0.864815, test set score:0.891667\n",
      "rbf SVM : C:19, train set score:0.864815, test set score:0.891667\n",
      "rbf SVM : C:20, train set score:0.864815, test set score:0.891667\n",
      "rbf SVM : C:21, train set score:0.864815, test set score:0.888889\n",
      "rbf SVM : C:22, train set score:0.864815, test set score:0.883333\n",
      "rbf SVM : C:23, train set score:0.864815, test set score:0.883333\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:993: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning: Solver terminated early (max_iter=1000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  warnings.warn(\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:993: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning: Solver terminated early (max_iter=1000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  warnings.warn(\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:993: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning: Solver terminated early (max_iter=1000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  warnings.warn(\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:993: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning: Solver terminated early (max_iter=1000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  warnings.warn(\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:993: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning: Solver terminated early (max_iter=1000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  warnings.warn(\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:993: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning: Solver terminated early (max_iter=1000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  warnings.warn(\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:993: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning: Solver terminated early (max_iter=1000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rbf SVM : C:24, train set score:0.862963, test set score:0.886111\n",
      "rbf SVM : C:25, train set score:0.861111, test set score:0.886111\n",
      "rbf SVM : C:26, train set score:0.861111, test set score:0.888889\n",
      "rbf SVM : C:27, train set score:0.862963, test set score:0.888889\n",
      "rbf SVM : C:28, train set score:0.861111, test set score:0.886111\n",
      "rbf SVM : C:29, train set score:0.861111, test set score:0.886111\n",
      "rbf SVM : C:30, train set score:0.861111, test set score:0.886111\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:993: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning: Solver terminated early (max_iter=1000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  warnings.warn(\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:993: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning: Solver terminated early (max_iter=1000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "# SVM C값 1~30에 따른 모델 성능 확인\n",
    "\n",
    "scores = []\n",
    "for C_point in [*range(1,31)]:\n",
    "    svc=SVC(kernel='rbf',C=C_point,max_iter = 1000)\n",
    "    C_model=svc.fit(x_train, y_train)\n",
    "    train_score = C_model.score(x_train, y_train)\n",
    "    test_score = C_model.score(x_test, y_test)\n",
    "    print(\"rbf SVM : C:{}, train set score:{:2f}, test set score:{:2f}\".format\n",
    "          (C_point,train_score, test_score))\n",
    "    scores.append([train_score, test_score])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f4f48ff2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rbf SVM : gamma:0.1, train set score:0.862963, test set score:0.866667\n",
      "rbf SVM : gamma:0.5, train set score:0.864815, test set score:0.880556\n",
      "rbf SVM : gamma:1, train set score:0.861111, test set score:0.877778\n",
      "rbf SVM : gamma:5, train set score:0.862963, test set score:0.880556\n",
      "rbf SVM : gamma:10, train set score:0.864815, test set score:0.883333\n",
      "rbf SVM : gamma:20, train set score:0.879630, test set score:0.872222\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:993: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:993: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:993: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:993: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:993: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:993: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning: Solver terminated early (max_iter=1000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "# SVM gamma값 1~30에 따른 모델 성능 확인\n",
    "\n",
    "scores = []\n",
    "for gamma_point in [0.1,0.5,1,5,10,20]:\n",
    "    svc=SVC(kernel='rbf',C=10,gamma=gamma_point,max_iter = 1000)\n",
    "    model=svc.fit(x_train, y_train)\n",
    "    train_score = model.score(x_train, y_train)\n",
    "    test_score = model.score(x_test, y_test)\n",
    "    print(\"rbf SVM : gamma:{}, train set score:{:2f}, test set score:{:2f}\".format\n",
    "          (gamma_point,train_score, test_score))\n",
    "    scores.append([train_score, test_score])"
   ]
  }
 ],
 "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}