13.6.1.KNN(K-Nearest Neighbors) (분류, 회귀 모델).ipynb

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   "id": "e9a5388c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: mglearn in c:\\users\\angry\\anaconda3\\lib\\site-packages (0.1.9)\n",
      "Requirement already satisfied: joblib in c:\\users\\angry\\anaconda3\\lib\\site-packages (from mglearn) (1.1.0)\n",
      "Requirement already satisfied: pillow in c:\\users\\angry\\anaconda3\\lib\\site-packages (from mglearn) (9.0.1)\n",
      "Requirement already satisfied: pandas in c:\\users\\angry\\anaconda3\\lib\\site-packages (from mglearn) (1.4.2)\n",
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      "Requirement already satisfied: scikit-learn in c:\\users\\angry\\anaconda3\\lib\\site-packages (from mglearn) (1.0.2)\n",
      "Requirement already satisfied: imageio in c:\\users\\angry\\anaconda3\\lib\\site-packages (from mglearn) (2.9.0)\n",
      "Requirement already satisfied: matplotlib in c:\\users\\angry\\anaconda3\\lib\\site-packages (from mglearn) (3.5.1)\n",
      "Requirement already satisfied: numpy in c:\\users\\angry\\anaconda3\\lib\\site-packages (from mglearn) (1.21.5)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in c:\\users\\angry\\anaconda3\\lib\\site-packages (from matplotlib->mglearn) (1.3.2)\n",
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      "Requirement already satisfied: pyparsing>=2.2.1 in c:\\users\\angry\\anaconda3\\lib\\site-packages (from matplotlib->mglearn) (3.0.4)\n",
      "Requirement already satisfied: packaging>=20.0 in c:\\users\\angry\\anaconda3\\lib\\site-packages (from matplotlib->mglearn) (21.3)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in c:\\users\\angry\\anaconda3\\lib\\site-packages (from matplotlib->mglearn) (2.8.2)\n",
      "Requirement already satisfied: six>=1.5 in c:\\users\\angry\\anaconda3\\lib\\site-packages (from python-dateutil>=2.7->matplotlib->mglearn) (1.16.0)\n",
      "Requirement already satisfied: pytz>=2020.1 in c:\\users\\angry\\anaconda3\\lib\\site-packages (from pandas->mglearn) (2021.3)\n",
      "Requirement already satisfied: scipy>=1.1.0 in c:\\users\\angry\\anaconda3\\lib\\site-packages (from scikit-learn->mglearn) (1.7.3)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in c:\\users\\angry\\anaconda3\\lib\\site-packages (from scikit-learn->mglearn) (2.2.0)\n"
     ]
    }
   ],
   "source": [
    "# 필요한 패키지 설치\n",
    "!pip install mglearn\n",
    "import mglearn\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.decomposition import PCA\n",
    "from matplotlib.colors import ListedColormap\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ed4928b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\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": "85bf3cf8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train data 개수:  540\n",
      "test data 개수:  360\n"
     ]
    }
   ],
   "source": [
    "# 독립변수와 종속변수 분리하여 생성\n",
    "x = df.drop(['Area','Class'], axis='columns')\n",
    "y = df[['Class']]\n",
    "\n",
    "# 독립변수 데이터 정규화 적용\n",
    "MinMaxScaler = MinMaxScaler()\n",
    "df_minmax = MinMaxScaler.fit_transform(x)\n",
    "\n",
    "# 컬럼명 결합\n",
    "x = pd.DataFrame(data=df_minmax, columns=x.columns)\n",
    "\n",
    "# 학습셋, 테스트셋 분리 (6:4)\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, train_size=0.6, test_size=0.4)\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": "2d3b98a0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8925925925925926\n",
      "0.8277777777777777\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n"
     ]
    }
   ],
   "source": [
    "# 기본 K-nn 모델 생성 및 적용\n",
    "\n",
    "classifier = KNeighborsClassifier(n_neighbors = 3, \n",
    "                                  weights='uniform', \n",
    "                                  metric=\"minkowski\")\n",
    "classifier.fit(x_train, y_train)\n",
    "\n",
    "# K-nn 모델 정확도 출력\n",
    "print(classifier.score(x_train, y_train))\n",
    "print(classifier.score(x_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "70035a94",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x1200 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 이웃 k 수 1 ~ 30까지 엘보우차트 시각화 (거리 가중치 미적용)\n",
    "\n",
    "# k 수 범위 지정\n",
    "k_num = range(1,31)\n",
    "\n",
    "accuracies = []\n",
    "for k in k_num:\n",
    "    k_num_model_1 = KNeighborsClassifier(n_neighbors = k, weights='uniform')\n",
    "    k_num_model_1.fit(x_train, y_train)\n",
    "    accuracies.append(1 - k_num_model_1.score(x_test, y_test))\n",
    "\n",
    "# 그래프 옵션 설정\n",
    "plt.plot(k_num, accuracies, 'o--', color = 'orange')\n",
    "plt.xlabel(\"k\")\n",
    "plt.ylabel(\"Misclassification Rate\")\n",
    "plt.ylim(0.14, 0.22)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "72ba2a71",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x1200 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 이웃 k 수 1 ~ 30까지 엘보우차트 시각화 (거리 가중치 적용)\n",
    "\n",
    "# k 수 범위 지정\n",
    "k_num = range(1,31)\n",
    "\n",
    "accuracies = []\n",
    "for k in k_num:\n",
    "  k_num_model_2 = KNeighborsClassifier(n_neighbors = k, weights='distance')\n",
    "  k_num_model_2.fit(x_train, y_train)\n",
    "  accuracies.append(1 - k_num_model_2.score(x_test, y_test))\n",
    "\n",
    "# 그래프 옵션 설정\n",
    "plt.plot(k_num, accuracies, 'o--', color = 'orange')\n",
    "plt.xlabel(\"k\")\n",
    "plt.ylabel(\"Misclassification Rate\")\n",
    "plt.ylim(0.14, 0.22)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "83153fcf",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n",
      "C:\\Users\\angry\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: 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",
      "  return self._fit(X, y)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x1200 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 이웃 k 수 1 ~ 200까지 엘보우차트 시각화 (거리 가중치 적용)\n",
    "\n",
    "# k 수 범위 지정\n",
    "k_num = range(1,201)\n",
    "\n",
    "accuracies = []\n",
    "for k in k_num:\n",
    "    k_num_model_2 = KNeighborsClassifier(n_neighbors = k, weights='distance')\n",
    "    k_num_model_2.fit(x_train, y_train)\n",
    "    accuracies.append(1 - k_num_model_2.score(x_test, y_test))\n",
    "\n",
    "# 그래프 옵션 설정\n",
    "plt.plot(k_num, accuracies, '--', color = 'orange')\n",
    "plt.xlabel(\"k\")\n",
    "plt.ylabel(\"Misclassification Rate\")\n",
    "plt.ylim(0.13, 0.17)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8bbba2f1",
   "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>C1</th>\n",
       "      <th>C2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.802409</td>\n",
       "      <td>0.242069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.045084</td>\n",
       "      <td>-0.098693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.318723</td>\n",
       "      <td>0.013834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.032614</td>\n",
       "      <td>0.046625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.017902</td>\n",
       "      <td>-0.204225</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         C1        C2\n",
       "0  0.802409  0.242069\n",
       "1 -0.045084 -0.098693\n",
       "2 -0.318723  0.013834\n",
       "3  0.032614  0.046625\n",
       "4 -0.017902 -0.204225"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 시각화를 위한 차원 축소\n",
    "\n",
    "# 주성분 개수 설정(2개 설정)\n",
    "pca = PCA(n_components=2) \n",
    "df_pca = pca.fit_transform(x_test)\n",
    "\n",
    "# 주성분으로 변형된 테이블 생성\n",
    "df_pca = pd.DataFrame(data=df_pca, columns = ['C1','C2'])\n",
    "\n",
    "df_pca.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "01d0bcf4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x20c87cab430>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 4500x1500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 결정 경계선(decision boundary) 시각화 확인\n",
    "\n",
    "# 독립변수, 종속변수 전처리\n",
    "df_vsl_x = df_pca.to_numpy()\n",
    "df_vsl_y = y_test['Class'].to_numpy()\n",
    "\n",
    "# 그래프 설정\n",
    "cmap_bold = ListedColormap(['#FF0000', '#00FF00'])\n",
    "fig, axes = plt.subplots(1, 3, figsize=(15, 5))\n",
    "\n",
    "# 이웃 수 1, 5, 20에 따른 결정곙계 시각화\n",
    "for n_neighbors, ax in zip([1, 5, 20], axes):\n",
    "    k_num_model_eg = KNeighborsClassifier(\n",
    "        n_neighbors=n_neighbors).fit(df_vsl_x, df_vsl_y)\n",
    "    mglearn.plots.plot_2d_separator(k_num_model_eg, df_vsl_x, \n",
    "                                    fill=True, eps=0.5, ax=ax, alpha=.2)\n",
    "    mglearn.discrete_scatter(df_vsl_x[:, 0], df_vsl_x[:, 1], \n",
    "                             df_vsl_y, markeredgewidth=0.1, \n",
    "                             c=['b','r'], s=3, ax=ax)\n",
    "    ax.set_xlabel(\"component 1\")\n",
    "    ax.set_ylabel(\"component 2\")\n",
    "    ax.set_title(\"{} Neighbors\".format(n_neighbors))\n",
    "axes[0].legend(loc=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e0482a5d",
   "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}