2_머신러닝.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "import matplotlib.font_manager as fm\n",
    "font_name = fm.FontProperties(fname=\"C:/Windows/Fonts/malgun.ttf\").get_name()\n",
    "plt.rc('font', family=font_name)\n",
    "\n",
    "mpl.rcParams[\"axes.unicode_minus\"] = False\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "import mglearn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 선형 회귀 모델\n",
    "\n",
    "hypothesis : y = w[0]*x[0] + w[1]*x[1] + ... + b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "w[0]: 0.393906  b: -0.031804\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mglearn.plots.plot_linear_regression_wave()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 회귀 분석용 샘플 호출\n",
    "X, y = mglearn.datasets.make_wave(n_samples=60)\n",
    "\n",
    "# 훈련용 데이터, 테스트용 데이터로 나누기\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 훈련 시작\n",
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 객체 생성\n",
    "lr = LinearRegression()\n",
    "\n",
    "# 학습\n",
    "lr.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "훈련 점수 :  0.6700890315075756\n",
      "테스트 점수 :  0.6593368596863701\n",
      "기울기 : [0.39390555], 절편 : -0.03180434302675973\n"
     ]
    }
   ],
   "source": [
    "# 훈련 점수 확인\n",
    "print(\"훈련 점수 : \", lr.score(X_train, y_train))\n",
    "\n",
    "# 테스트 점수 확인(과적합 여부)\n",
    "print(\"테스트 점수 : \", lr.score(X_test, y_test))\n",
    "\n",
    "# 기울기 확인\n",
    "dir(lr)\n",
    "print(\"기울기 : {}, 절편 : {}\".format(lr.coef_, lr.intercept_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 주제 : 전기 생산량과 소비량\n",
    "# 귀무가설 : 전기 생산량과 소비량과 전기 소비량의 상관 관계가 없다.\n",
    "# 대립가설 : 전기 생산량과 소비량과 전기 소비량의 상관 관계가 있다.\n",
    "\n",
    "# 월별 전기 사용량(백만kwh)\n",
    "y = [2.48, 2.27, 2.47, 2.77, 2.98, 3.05, 3.18, 3.46, 3.03, 3.25, 2.67, 2.53]\n",
    "\n",
    "# 월별 전기 생산금액(억원)\n",
    "X = [3.52, 2.58, 3.31, 4.07, 4.62, 3.98, 4.29, 4.83, 3.71, 4.61, 3.90, 3.20]\n",
    "\n",
    "# 생산량 4일때 사용량은 얼마인가?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "all the input array dimensions for the concatenation axis must match exactly, but along dimension 1, the array at index 0 has size 2 and the array at index 1 has size 100",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-40-15202a6e9c9b>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mscipy\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mstats\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mstats\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlinregress\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\scipy\\stats\\_stats_mstats_common.py\u001b[0m in \u001b[0;36mlinregress\u001b[1;34m(x, y)\u001b[0m\n\u001b[0;32m    114\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    115\u001b[0m     \u001b[1;31m# average sum of squares:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 116\u001b[1;33m     \u001b[0mssxm\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mssxym\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mssyxm\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mssym\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcov\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbias\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mflat\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    117\u001b[0m     \u001b[0mr_num\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mssxym\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    118\u001b[0m     \u001b[0mr_den\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msqrt\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mssxm\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0mssym\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<__array_function__ internals>\u001b[0m in \u001b[0;36mcov\u001b[1;34m(*args, **kwargs)\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\numpy\\lib\\function_base.py\u001b[0m in \u001b[0;36mcov\u001b[1;34m(m, y, rowvar, bias, ddof, fweights, aweights)\u001b[0m\n\u001b[0;32m   2388\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mrowvar\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2389\u001b[0m             \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mT\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2390\u001b[1;33m         \u001b[0mX\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconcatenate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2391\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2392\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mddof\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<__array_function__ internals>\u001b[0m in \u001b[0;36mconcatenate\u001b[1;34m(*args, **kwargs)\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: all the input array dimensions for the concatenation axis must match exactly, but along dimension 1, the array at index 0 has size 2 and the array at index 1 has size 100"
     ]
    }
   ],
   "source": [
    "from scipy import stats\n",
    "\n",
    "result = stats.linregress(X, y)\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.49560323601829054 0.9195814280689416\n"
     ]
    }
   ],
   "source": [
    "# 각 값들을 언박싱\n",
    "slope, intercept, rv, pv, stderr = stats.linregress(X, y)\n",
    "print(slope, intercept)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x18203831c40>]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 그래프로 확인\n",
    "plt.scatter(X, y)\n",
    "plt.plot(np.array(X), slope*np.array(X)+intercept, c=\"red\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "결과 :  2.901994372142104\n"
     ]
    }
   ],
   "source": [
    "# 생산량 4일때 사용량은 얼마인가?\n",
    "result = slope*4 + intercept\n",
    "print(\"결과 : \", result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.49560324] , 0.919581428068942\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([2.90199437])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 머신러닝 알고리즘 사용\n",
    "num = np.array(X)\n",
    "num = num.reshape(-1, 1)\n",
    "\n",
    "lr = LinearRegression()\n",
    "lr.fit(num, y)\n",
    "\n",
    "lr2 = LinearRegression()\n",
    "lr2.fit(num, y)\n",
    "print(lr2.coef_, \",\", lr2.intercept_)\n",
    "\n",
    "# 예측값으로 확인\n",
    "lr2.predict([[4]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 고차원 특성에 대한 과적합"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(506, 104)\n",
      "훈련 점수 : 0.9520519609032729\n",
      "테스트 점수 : 0.6074721959665842\n"
     ]
    }
   ],
   "source": [
    "from mglearn.datasets import load_boston, load_extended_boston\n",
    "\n",
    "X, y = load_extended_boston()\n",
    "print(X.shape)\n",
    "\n",
    "# 데이터를 훈련용과 테스트용으로 나누기\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)\n",
    "\n",
    "# 훈련 시작\n",
    "lr = LinearRegression().fit(X_train, y_train)\n",
    "\n",
    "# 훈련점수, 테스트점수 확인(MSE)\n",
    "print(\"훈련 점수 :\", lr.score(X_train, y_train))\n",
    "print(\"테스트 점수 :\", lr.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 릿지(Ridge) 회귀 모델\n",
    "\n",
    "- L2 규제(Regularization)\n",
    "- MSE항에 Penalty항 추가\n",
    "- 알파를 크게 하면 패널티의 효과가 커지고(가중치 감소), 작게하면 그 반대가 된다.\n",
    "- 모델을 단순하게 해주고 훈련셋에 대한 성능 차이를 절충하는 효과"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "훈련 점수 : 0.885796658517094\n",
      "테스트 점수 : 0.7527683481744752\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import Ridge\n",
    "\n",
    "ridge = Ridge(alpha=1.0).fit(X_train, y_train)  # 기본값 alpha=1.0\n",
    "\n",
    "# 훈련점수, 테스트점수 확인(릿지 회귀)\n",
    "print(\"훈련 점수 :\", ridge.score(X_train, y_train))\n",
    "print(\"테스트 점수 :\", ridge.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "훈련 점수 : 0.7882787115369614\n",
      "테스트 점수 : 0.6359411489177309\n"
     ]
    }
   ],
   "source": [
    "# 하이퍼 파라미터(alpha=10)값 조정\n",
    "ridge10 = Ridge(alpha=10).fit(X_train, y_train)\n",
    "print(\"훈련 점수 :\", ridge10.score(X_train, y_train))\n",
    "print(\"테스트 점수 :\", ridge10.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "훈련 점수 : 0.9282273685001982\n",
      "테스트 점수 : 0.7722067936479957\n"
     ]
    }
   ],
   "source": [
    "# 하이퍼 파라미터(alpha=0.1)값 조정\n",
    "ridge01 = Ridge(alpha=0.1).fit(X_train, y_train)\n",
    "print(\"훈련 점수 :\", ridge01.score(X_train, y_train))\n",
    "print(\"테스트 점수 :\", ridge01.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x182056fde80>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 그래프\n",
    "plt.plot(ridge10.coef_, '^', label=\"Ridge alpha=10\")\n",
    "plt.plot(ridge.coef_, 's', label=\"Ridge alpha=1\")\n",
    "plt.plot(ridge01.coef_, 'v', label=\"Ridge alpha=0.1\")\n",
    "\n",
    "plt.plot(lr.coef_, 'o', label=\"Linear Regression\")\n",
    "\n",
    "plt.xlabel(\"계수 목록\")\n",
    "plt.ylabel(\"계수 크기\")\n",
    "plt.hlines(0, 0, len(lr.coef_))\n",
    "plt.ylim(-25, 25)\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 알파값을 1로 고정한 상태에서 데이터의 크기 조절\n",
    "mglearn.plots.plot_ridge_n_samples()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 라쏘(Lasso) 회귀 모델\n",
    "\n",
    "- L1 규제 사용\n",
    "- 알파를 크게하면 패널티의 효과가 커지고 작게하면 그 반대가 된다.\n",
    "- 특성 선택이 자동으로 이루어진다.(가중치를 0으로 해서)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import Lasso"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "훈련 점수 : 0.29323768991114607\n",
      "테스트 점수 : 0.20937503255272294\n",
      "사용한 특성의 수 :  4\n"
     ]
    }
   ],
   "source": [
    "lasso = Lasso().fit(X_train, y_train) # alpha=1.0\n",
    "print(\"훈련 점수 :\", lasso.score(X_train, y_train))\n",
    "print(\"테스트 점수 :\", lasso.score(X_test, y_test))\n",
    "print(\"사용한 특성의 수 : \", np.sum(lasso.coef_ != 0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "훈련 점수 : 0.8962226511086501\n",
      "테스트 점수 : 0.7656571174549989\n",
      "사용한 특성의 수 :  33\n"
     ]
    }
   ],
   "source": [
    "# 알파값을 낮추면 반복횟수는 증가\n",
    "lasso001 = Lasso(alpha=0.01, max_iter=100000).fit(X_train, y_train) \n",
    "print(\"훈련 점수 :\", lasso001.score(X_train, y_train))\n",
    "print(\"테스트 점수 :\", lasso001.score(X_test, y_test))\n",
    "print(\"사용한 특성의 수 : \", np.sum(lasso001.coef_ != 0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "훈련 점수 : 0.9507158754515462\n",
      "테스트 점수 : 0.6437467421273407\n",
      "사용한 특성의 수 :  96\n"
     ]
    }
   ],
   "source": [
    "lasso00001 = Lasso(alpha=0.0001, max_iter=100000).fit(X_train, y_train) \n",
    "print(\"훈련 점수 :\", lasso00001.score(X_train, y_train))\n",
    "print(\"테스트 점수 :\", lasso00001.score(X_test, y_test))\n",
    "print(\"사용한 특성의 수 : \", np.sum(lasso00001.coef_ != 0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ElasticNet\n",
    "\n",
    "- Ridge + Lasso"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "### Boston Sample을 이용한 다중 선형 회귀"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(506, 13)\n",
      "['CRIM' 'ZN' 'INDUS' 'CHAS' 'NOX' 'RM' 'AGE' 'DIS' 'RAD' 'TAX' 'PTRATIO'\n",
      " 'B' 'LSTAT']\n",
      "[24.  21.6 34.7 33.4 36.2 28.7 22.9 27.1 16.5 18.9 15.  18.9 21.7 20.4\n",
      " 18.2 19.9 23.1 17.5 20.2 18.2 13.6 19.6 15.2 14.5 15.6 13.9 16.6 14.8\n",
      " 18.4 21.  12.7 14.5 13.2 13.1 13.5 18.9 20.  21.  24.7 30.8 34.9 26.6\n",
      " 25.3 24.7 21.2 19.3 20.  16.6 14.4 19.4 19.7 20.5 25.  23.4 18.9 35.4\n",
      " 24.7 31.6 23.3 19.6 18.7 16.  22.2 25.  33.  23.5 19.4 22.  17.4 20.9\n",
      " 24.2 21.7 22.8 23.4 24.1 21.4 20.  20.8 21.2 20.3 28.  23.9 24.8 22.9\n",
      " 23.9 26.6 22.5 22.2 23.6 28.7 22.6 22.  22.9 25.  20.6 28.4 21.4 38.7\n",
      " 43.8 33.2 27.5 26.5 18.6 19.3 20.1 19.5 19.5 20.4 19.8 19.4 21.7 22.8\n",
      " 18.8 18.7 18.5 18.3 21.2 19.2 20.4 19.3 22.  20.3 20.5 17.3 18.8 21.4\n",
      " 15.7 16.2 18.  14.3 19.2 19.6 23.  18.4 15.6 18.1 17.4 17.1 13.3 17.8\n",
      " 14.  14.4 13.4 15.6 11.8 13.8 15.6 14.6 17.8 15.4 21.5 19.6 15.3 19.4\n",
      " 17.  15.6 13.1 41.3 24.3 23.3 27.  50.  50.  50.  22.7 25.  50.  23.8\n",
      " 23.8 22.3 17.4 19.1 23.1 23.6 22.6 29.4 23.2 24.6 29.9 37.2 39.8 36.2\n",
      " 37.9 32.5 26.4 29.6 50.  32.  29.8 34.9 37.  30.5 36.4 31.1 29.1 50.\n",
      " 33.3 30.3 34.6 34.9 32.9 24.1 42.3 48.5 50.  22.6 24.4 22.5 24.4 20.\n",
      " 21.7 19.3 22.4 28.1 23.7 25.  23.3 28.7 21.5 23.  26.7 21.7 27.5 30.1\n",
      " 44.8 50.  37.6 31.6 46.7 31.5 24.3 31.7 41.7 48.3 29.  24.  25.1 31.5\n",
      " 23.7 23.3 22.  20.1 22.2 23.7 17.6 18.5 24.3 20.5 24.5 26.2 24.4 24.8\n",
      " 29.6 42.8 21.9 20.9 44.  50.  36.  30.1 33.8 43.1 48.8 31.  36.5 22.8\n",
      " 30.7 50.  43.5 20.7 21.1 25.2 24.4 35.2 32.4 32.  33.2 33.1 29.1 35.1\n",
      " 45.4 35.4 46.  50.  32.2 22.  20.1 23.2 22.3 24.8 28.5 37.3 27.9 23.9\n",
      " 21.7 28.6 27.1 20.3 22.5 29.  24.8 22.  26.4 33.1 36.1 28.4 33.4 28.2\n",
      " 22.8 20.3 16.1 22.1 19.4 21.6 23.8 16.2 17.8 19.8 23.1 21.  23.8 23.1\n",
      " 20.4 18.5 25.  24.6 23.  22.2 19.3 22.6 19.8 17.1 19.4 22.2 20.7 21.1\n",
      " 19.5 18.5 20.6 19.  18.7 32.7 16.5 23.9 31.2 17.5 17.2 23.1 24.5 26.6\n",
      " 22.9 24.1 18.6 30.1 18.2 20.6 17.8 21.7 22.7 22.6 25.  19.9 20.8 16.8\n",
      " 21.9 27.5 21.9 23.1 50.  50.  50.  50.  50.  13.8 13.8 15.  13.9 13.3\n",
      " 13.1 10.2 10.4 10.9 11.3 12.3  8.8  7.2 10.5  7.4 10.2 11.5 15.1 23.2\n",
      "  9.7 13.8 12.7 13.1 12.5  8.5  5.   6.3  5.6  7.2 12.1  8.3  8.5  5.\n",
      " 11.9 27.9 17.2 27.5 15.  17.2 17.9 16.3  7.   7.2  7.5 10.4  8.8  8.4\n",
      " 16.7 14.2 20.8 13.4 11.7  8.3 10.2 10.9 11.   9.5 14.5 14.1 16.1 14.3\n",
      " 11.7 13.4  9.6  8.7  8.4 12.8 10.5 17.1 18.4 15.4 10.8 11.8 14.9 12.6\n",
      " 14.1 13.  13.4 15.2 16.1 17.8 14.9 14.1 12.7 13.5 14.9 20.  16.4 17.7\n",
      " 19.5 20.2 21.4 19.9 19.  19.1 19.1 20.1 19.9 19.6 23.2 29.8 13.8 13.3\n",
      " 16.7 12.  14.6 21.4 23.  23.7 25.  21.8 20.6 21.2 19.1 20.6 15.2  7.\n",
      "  8.1 13.6 20.1 21.8 24.5 23.1 19.7 18.3 21.2 17.5 16.8 22.4 20.6 23.9\n",
      " 22.  11.9]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.datasets import load_boston\n",
    "\n",
    "boston = load_boston()\n",
    "print(boston.data.shape)\n",
    "print(boston.feature_names)\n",
    "print(boston.target)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#![title](img/Pasted Image 2021-01-08 14-40-57.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "\n",
    "Image(\"Pasted Image 2021-01-08 14-40-57.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_boston = LinearRegression().fit(boston.data, boston.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1.08011358e-01,  4.64204584e-02,  2.05586264e-02,  2.68673382e+00,\n",
       "       -1.77666112e+01,  3.80986521e+00,  6.92224640e-04, -1.47556685e+00,\n",
       "        3.06049479e-01, -1.23345939e-02, -9.52747232e-01,  9.31168327e-03,\n",
       "       -5.24758378e-01])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_boston.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 예측값\n",
    "pred = model_boston.predict(boston.data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x18205df10d0>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 그래프\n",
    "plt.scatter(boston.target, pred) #(X, y)\n",
    "plt.xlabel(\"실제 집값\")\n",
    "plt.ylabel(\"예측 집값\")\n",
    "plt.title(\"집값 예측치와 실제 집값의 관계\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": true
   },
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "      <th>MEDV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00632</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>6.575</td>\n",
       "      <td>65.2</td>\n",
       "      <td>4.0900</td>\n",
       "      <td>1.0</td>\n",
       "      <td>296.0</td>\n",
       "      <td>15.3</td>\n",
       "      <td>396.90</td>\n",
       "      <td>4.98</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.02731</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>6.421</td>\n",
       "      <td>78.9</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>396.90</td>\n",
       "      <td>9.14</td>\n",
       "      <td>21.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.02729</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>7.185</td>\n",
       "      <td>61.1</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>392.83</td>\n",
       "      <td>4.03</td>\n",
       "      <td>34.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.03237</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>6.998</td>\n",
       "      <td>45.8</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>394.63</td>\n",
       "      <td>2.94</td>\n",
       "      <td>33.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.06905</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>7.147</td>\n",
       "      <td>54.2</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.33</td>\n",
       "      <td>36.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.02985</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>6.430</td>\n",
       "      <td>58.7</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>394.12</td>\n",
       "      <td>5.21</td>\n",
       "      <td>28.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.08829</td>\n",
       "      <td>12.5</td>\n",
       "      <td>7.87</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.524</td>\n",
       "      <td>6.012</td>\n",
       "      <td>66.6</td>\n",
       "      <td>5.5605</td>\n",
       "      <td>5.0</td>\n",
       "      <td>311.0</td>\n",
       "      <td>15.2</td>\n",
       "      <td>395.60</td>\n",
       "      <td>12.43</td>\n",
       "      <td>22.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.14455</td>\n",
       "      <td>12.5</td>\n",
       "      <td>7.87</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.524</td>\n",
       "      <td>6.172</td>\n",
       "      <td>96.1</td>\n",
       "      <td>5.9505</td>\n",
       "      <td>5.0</td>\n",
       "      <td>311.0</td>\n",
       "      <td>15.2</td>\n",
       "      <td>396.90</td>\n",
       "      <td>19.15</td>\n",
       "      <td>27.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.21124</td>\n",
       "      <td>12.5</td>\n",
       "      <td>7.87</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.524</td>\n",
       "      <td>5.631</td>\n",
       "      <td>100.0</td>\n",
       "      <td>6.0821</td>\n",
       "      <td>5.0</td>\n",
       "      <td>311.0</td>\n",
       "      <td>15.2</td>\n",
       "      <td>386.63</td>\n",
       "      <td>29.93</td>\n",
       "      <td>16.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.17004</td>\n",
       "      <td>12.5</td>\n",
       "      <td>7.87</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.524</td>\n",
       "      <td>6.004</td>\n",
       "      <td>85.9</td>\n",
       "      <td>6.5921</td>\n",
       "      <td>5.0</td>\n",
       "      <td>311.0</td>\n",
       "      <td>15.2</td>\n",
       "      <td>386.71</td>\n",
       "      <td>17.10</td>\n",
       "      <td>18.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      CRIM    ZN INDUS CHAS    NOX     RM    AGE     DIS  RAD    TAX PTRATIO  \\\n",
       "0  0.00632  18.0  2.31  0.0  0.538  6.575   65.2  4.0900  1.0  296.0    15.3   \n",
       "1  0.02731   0.0  7.07  0.0  0.469  6.421   78.9  4.9671  2.0  242.0    17.8   \n",
       "2  0.02729   0.0  7.07  0.0  0.469  7.185   61.1  4.9671  2.0  242.0    17.8   \n",
       "3  0.03237   0.0  2.18  0.0  0.458  6.998   45.8  6.0622  3.0  222.0    18.7   \n",
       "4  0.06905   0.0  2.18  0.0  0.458  7.147   54.2  6.0622  3.0  222.0    18.7   \n",
       "5  0.02985   0.0  2.18  0.0  0.458  6.430   58.7  6.0622  3.0  222.0    18.7   \n",
       "6  0.08829  12.5  7.87  0.0  0.524  6.012   66.6  5.5605  5.0  311.0    15.2   \n",
       "7  0.14455  12.5  7.87  0.0  0.524  6.172   96.1  5.9505  5.0  311.0    15.2   \n",
       "8  0.21124  12.5  7.87  0.0  0.524  5.631  100.0  6.0821  5.0  311.0    15.2   \n",
       "9  0.17004  12.5  7.87  0.0  0.524  6.004   85.9  6.5921  5.0  311.0    15.2   \n",
       "\n",
       "        B  LSTAT  MEDV  \n",
       "0  396.90   4.98  24.0  \n",
       "1  396.90   9.14  21.6  \n",
       "2  392.83   4.03  34.7  \n",
       "3  394.63   2.94  33.4  \n",
       "4  396.90   5.33  36.2  \n",
       "5  394.12   5.21  28.7  \n",
       "6  395.60  12.43  22.9  \n",
       "7  396.90  19.15  27.1  \n",
       "8  386.63  29.93  16.5  \n",
       "9  386.71  17.10  18.9  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(boston.data, columns=[boston.feature_names])\n",
    "df.head(10)\n",
    "\n",
    "# 종속변수(집값) 추가\n",
    "df[\"MEDV\"] = boston.target\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\cecil\\anaconda3\\lib\\site-packages\\seaborn\\axisgrid.py:2071: UserWarning: The `size` parameter has been renamed to `height`; please update your code.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x182037594f0>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x900 with 30 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# seaborn의 pairplot\n",
    "cols = [\"LSTAT\", \"INDUS\", \"NOX\", \"RM\", \"MEDV\"]\n",
    "sns.pairplot(df[cols], size=2.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x18206f6b8e0>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# seaborn의 heatmap\n",
    "\n",
    "# 상관계수 구하기\n",
    "cc = np.corrcoef(df[cols].values.T) # 컬럼별로 나오게 .T\n",
    "cc\n",
    "sns.heatmap(cc, yticklabels=cols, xticklabels=cols, annot=True, square=True, annot_kws={\"size\":15}) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[9.10210898]] [-34.67062078]\n",
      "방이 [5]인 주택가격은 약 [10840]달러이다.\n"
     ]
    }
   ],
   "source": [
    "# 방이 5개일 때 집값은 얼마인가?\n",
    "X = df[[\"RM\"]].values\n",
    "y = df[[\"MEDV\"]].values\n",
    "\n",
    "lr = LinearRegression().fit(X, y)\n",
    "print(lr.coef_, lr.intercept_)\n",
    "\n",
    "house_price = lr.predict([[5]])\n",
    "\n",
    "print(\"방이 [%d]인 주택가격은 약 [%.f]달러이다.\"%(5, house_price*1000))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 로지스틱 선형 회귀\n",
    "\n",
    "- L2 norm 규제를 사용\n",
    "- C 파라미터 사용해서 규제의 강도를 설정"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1820738cfd0>]"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# sigmoid 함수 그래프 그리기\n",
    "\n",
    "def sigmoid(x):\n",
    "    result = []\n",
    "    for i in x:\n",
    "        result.append(1/(1+np.exp(-i)))  # 시그모이드 공식\n",
    "        \n",
    "    return result\n",
    "\n",
    "\n",
    "x = np.linspace(-8, 8, 100)\n",
    "sig = sigmoid(x)\n",
    "\n",
    "plt.plot(x, sig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x182073ed2b0>]"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(-5, 5, 1000)\n",
    "plt.plot(x, 1/(1+np.exp(-x)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import LinearSVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\cecil\\anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:86: FutureWarning: Function make_blobs is deprecated; Please import make_blobs directly from scikit-learn\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "C:\\Users\\cecil\\anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:976: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\"Liblinear failed to converge, increase \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x216 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X, y = mglearn.datasets.make_forge()\n",
    "fig, axes = plt.subplots(1, 2, figsize=(10, 3)) # fig는 _로 대체 가능\n",
    "\n",
    "for model, ax in zip([LinearSVC(C=1), LogisticRegression(C=1)], axes):\n",
    "    clf = model.fit(X, y)\n",
    "    \n",
    "    mglearn.plots.plot_2d_separator(clf, X, ax=ax)\n",
    "    mglearn.discrete_scatter(X[:, 0], X[:, 1], y, ax=ax)\n",
    "    \n",
    "    ax.set_title(\"{}\".format(clf.__class__.__name__))\n",
    "    ax.set_ylabel(\"특성0\")\n",
    "    ax.set_xlabel(\"특성1\")\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mglearn.plots.plot_linear_svc_regularization()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### c값에 따른 결과"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_breast_cancer\n",
    "\n",
    "cancer = load_breast_cancer()\n",
    "X_train, X_test, y_train, y_test = train_test_split(cancer.data, cancer.target, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "훈련 점수 :  0.9577464788732394\n",
      "테스트 점수 :  0.965034965034965\n"
     ]
    }
   ],
   "source": [
    "logreg = LogisticRegression(C=1, penalty=\"l2\", max_iter=10000).fit(X_train, y_train)\n",
    "print(\"훈련 점수 : \", logreg.score(X_train, y_train))\n",
    "print(\"테스트 점수 : \", logreg.score(X_test, y_test))\n",
    "\n",
    "# 과소적합 의심"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "훈련 점수 :  0.9788732394366197\n",
      "테스트 점수 :  0.965034965034965\n"
     ]
    }
   ],
   "source": [
    "logreg100 = LogisticRegression(C=100, penalty=\"l2\", max_iter=10000).fit(X_train, y_train)\n",
    "print(\"훈련 점수 : \", logreg100.score(X_train, y_train))\n",
    "print(\"테스트 점수 : \", logreg100.score(X_test, y_test))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "훈련 점수 :  0.9882629107981221\n",
      "테스트 점수 :  0.972027972027972\n"
     ]
    }
   ],
   "source": [
    "# 'new-cg', 'lbfgs', 'sag', 'saga', handle L@ or no penalty\n",
    "# 'liblinear' and 'saga' also handle L1 penalty\n",
    "\n",
    "logreg100_l1 = LogisticRegression(C=100, penalty=\"l1\", max_iter=10000, solver=\"liblinear\").fit(X_train, y_train)\n",
    "print(\"훈련 점수 : \", logreg100_l1.score(X_train, y_train))\n",
    "print(\"테스트 점수 : \", logreg100_l1.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "훈련 점수 :  0.9107981220657277\n",
      "테스트 점수 :  0.951048951048951\n"
     ]
    }
   ],
   "source": [
    "logreg001_l1 = LogisticRegression(C=0.01, penalty=\"l1\", max_iter=10000, solver=\"liblinear\").fit(X_train, y_train)\n",
    "print(\"훈련 점수 : \", logreg001_l1.score(X_train, y_train))\n",
    "print(\"테스트 점수 : \", logreg001_l1.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### KNN, LogisticRegression, LinearSVC의 성능비교"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([212, 357], dtype=int64)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir(cancer)\n",
    "cancer.data.shape\n",
    "cancer.feature_names\n",
    "cancer.target_names   # malignan(악성), benign(양성)\n",
    "np.bincount(cancer.target)\n",
    "# cancer.target[cancer.target==0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# LogisticRegression\n",
    "# 표본을 어떻게 뽑느냐에따라 결과가 달라질 수 있음(cross_valuation??)\n",
    "\n",
    "for i in range(10):\n",
    "    X_train, X_test, y_train, y_test = train_test_split(cancer.data, cancer.target)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "scores =  0.951048951048951\n"
     ]
    }
   ],
   "source": [
    "# LogisticRegression\n",
    "X_train, X_test, y_train, y_test = train_test_split(cancer.data, \n",
    "                                                    cancer.target,\n",
    "                                                   random_state=0)\n",
    "model = LogisticRegression(max_iter=10000).fit(X_train, y_train)\n",
    "scores = model.score(X_test, y_test)\n",
    "\n",
    "print(\"scores = \", scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\cecil\\anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:976: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\"Liblinear failed to converge, increase \"\n",
      "C:\\Users\\cecil\\anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:976: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\"Liblinear failed to converge, increase \"\n",
      "C:\\Users\\cecil\\anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:976: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\"Liblinear failed to converge, increase \"\n",
      "C:\\Users\\cecil\\anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:976: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\"Liblinear failed to converge, increase \"\n",
      "C:\\Users\\cecil\\anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:976: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\"Liblinear failed to converge, increase \"\n",
      "C:\\Users\\cecil\\anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:976: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\"Liblinear failed to converge, increase \"\n",
      "C:\\Users\\cecil\\anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:976: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\"Liblinear failed to converge, increase \"\n",
      "C:\\Users\\cecil\\anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:976: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\"Liblinear failed to converge, increase \"\n",
      "C:\\Users\\cecil\\anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:976: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\"Liblinear failed to converge, increase \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "scores =  [0.9370629370629371, 0.9440559440559441, 0.6573426573426573, 0.9370629370629371, 0.9230769230769231, 0.916083916083916, 0.9020979020979021, 0.9230769230769231, 0.9440559440559441, 0.9230769230769231]\n",
      "0.9006993006993007\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\cecil\\anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:976: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\"Liblinear failed to converge, increase \"\n"
     ]
    }
   ],
   "source": [
    "# LinearSVC\n",
    "scores = []\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(cancer.data, cancer.target,\n",
    "                                                   random_state=0)\n",
    "\n",
    "for i in range(10):\n",
    "    model = LinearSVC(max_iter=10000).fit(X_train, y_train)\n",
    "    scores.append(model.score(X_test, y_test))\n",
    "\n",
    "print(\"scores = \", scores)\n",
    "print(np.mean(np.array(scores)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "scores =  0.9370629370629371\n"
     ]
    }
   ],
   "source": [
    "# KNN\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(cancer.data, cancer.target,\n",
    "                                                   random_state=0)\n",
    "\n",
    "model = KNeighborsClassifier(n_neighbors=5).fit(X_train, y_train)\n",
    "scores = model.score(X_test, y_test)\n",
    "\n",
    "print(\"scores = \", scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "  <matplotlib.axis.XTick at 0x210d9193760>,\n",
       "  <matplotlib.axis.XTick at 0x210d6eada60>,\n",
       "  <matplotlib.axis.XTick at 0x210d70b75e0>,\n",
       "  <matplotlib.axis.XTick at 0x210d70b7af0>,\n",
       "  <matplotlib.axis.XTick at 0x210d70ba040>,\n",
       "  <matplotlib.axis.XTick at 0x210d70ba550>,\n",
       "  <matplotlib.axis.XTick at 0x210d70baa90>,\n",
       "  <matplotlib.axis.XTick at 0x210d70b7670>,\n",
       "  <matplotlib.axis.XTick at 0x210d70ba6d0>,\n",
       "  <matplotlib.axis.XTick at 0x210d70bf190>,\n",
       "  <matplotlib.axis.XTick at 0x210d70bf6a0>,\n",
       "  <matplotlib.axis.XTick at 0x210d70bfbb0>,\n",
       "  <matplotlib.axis.XTick at 0x210d70c4100>,\n",
       "  <matplotlib.axis.XTick at 0x210d70c4610>,\n",
       "  <matplotlib.axis.XTick at 0x210d70c4b20>,\n",
       "  <matplotlib.axis.XTick at 0x210d70ca070>,\n",
       "  <matplotlib.axis.XTick at 0x210d70c4880>,\n",
       "  <matplotlib.axis.XTick at 0x210d70bf910>,\n",
       "  <matplotlib.axis.XTick at 0x210d70b72e0>,\n",
       "  <matplotlib.axis.XTick at 0x210d70ca730>,\n",
       "  <matplotlib.axis.XTick at 0x210d70cac40>,\n",
       "  <matplotlib.axis.XTick at 0x210d70d0190>,\n",
       "  <matplotlib.axis.XTick at 0x210d70d06a0>,\n",
       "  <matplotlib.axis.XTick at 0x210d70d0bb0>,\n",
       "  <matplotlib.axis.XTick at 0x210d70d6100>,\n",
       "  <matplotlib.axis.XTick at 0x210d70d6610>,\n",
       "  <matplotlib.axis.XTick at 0x210d70d0880>,\n",
       "  <matplotlib.axis.XTick at 0x210d70ca910>,\n",
       "  <matplotlib.axis.XTick at 0x210d70c4190>],\n",
       " [Text(0, 0, 'mean radius'),\n",
       "  Text(0, 0, 'mean texture'),\n",
       "  Text(0, 0, 'mean perimeter'),\n",
       "  Text(0, 0, 'mean area'),\n",
       "  Text(0, 0, 'mean smoothness'),\n",
       "  Text(0, 0, 'mean compactness'),\n",
       "  Text(0, 0, 'mean concavity'),\n",
       "  Text(0, 0, 'mean concave points'),\n",
       "  Text(0, 0, 'mean symmetry'),\n",
       "  Text(0, 0, 'mean fractal dimension'),\n",
       "  Text(0, 0, 'radius error'),\n",
       "  Text(0, 0, 'texture error'),\n",
       "  Text(0, 0, 'perimeter error'),\n",
       "  Text(0, 0, 'area error'),\n",
       "  Text(0, 0, 'smoothness error'),\n",
       "  Text(0, 0, 'compactness error'),\n",
       "  Text(0, 0, 'concavity error'),\n",
       "  Text(0, 0, 'concave points error'),\n",
       "  Text(0, 0, 'symmetry error'),\n",
       "  Text(0, 0, 'fractal dimension error'),\n",
       "  Text(0, 0, 'worst radius'),\n",
       "  Text(0, 0, 'worst texture'),\n",
       "  Text(0, 0, 'worst perimeter'),\n",
       "  Text(0, 0, 'worst area'),\n",
       "  Text(0, 0, 'worst smoothness'),\n",
       "  Text(0, 0, 'worst compactness'),\n",
       "  Text(0, 0, 'worst concavity'),\n",
       "  Text(0, 0, 'worst concave points'),\n",
       "  Text(0, 0, 'worst symmetry'),\n",
       "  Text(0, 0, 'worst fractal dimension')])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 이상치 검출을 위한 boxplot\n",
    "plt.figure(figsize=(10, 8))\n",
    "plt.boxplot(cancer.data)\n",
    "plt.xticks(np.arange(30)+1, cancer.feature_names, rotation=90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "### standardization\n",
    "\n",
    "# 속성별 평균\n",
    "m = cancer.data.mean(axis=0)\n",
    "\n",
    "# 속성별 표준편차\n",
    "s = cancer.data.std(axis=0)\n",
    "\n",
    "data1 = (cancer.data - m) / s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.XTick at 0x210d6943850>,\n",
       "  <matplotlib.axis.XTick at 0x210d72eafd0>,\n",
       "  <matplotlib.axis.XTick at 0x210d73183d0>,\n",
       "  <matplotlib.axis.XTick at 0x210d74f6b50>,\n",
       "  <matplotlib.axis.XTick at 0x210d74fb0a0>,\n",
       "  <matplotlib.axis.XTick at 0x210d7318340>,\n",
       "  <matplotlib.axis.XTick at 0x210d74fb760>,\n",
       "  <matplotlib.axis.XTick at 0x210d74fbc70>,\n",
       "  <matplotlib.axis.XTick at 0x210d75011c0>,\n",
       "  <matplotlib.axis.XTick at 0x210d75016d0>,\n",
       "  <matplotlib.axis.XTick at 0x210d7501be0>,\n",
       "  <matplotlib.axis.XTick at 0x210d7505130>,\n",
       "  <matplotlib.axis.XTick at 0x210d7505640>,\n",
       "  <matplotlib.axis.XTick at 0x210d7505b50>,\n",
       "  <matplotlib.axis.XTick at 0x210d7501310>,\n",
       "  <matplotlib.axis.XTick at 0x210d74fb880>,\n",
       "  <matplotlib.axis.XTick at 0x210d75052b0>,\n",
       "  <matplotlib.axis.XTick at 0x210d750d250>,\n",
       "  <matplotlib.axis.XTick at 0x210d750d760>,\n",
       "  <matplotlib.axis.XTick at 0x210d750dc70>,\n",
       "  <matplotlib.axis.XTick at 0x210d75131c0>,\n",
       "  <matplotlib.axis.XTick at 0x210d75136d0>,\n",
       "  <matplotlib.axis.XTick at 0x210d7513be0>,\n",
       "  <matplotlib.axis.XTick at 0x210d7517130>,\n",
       "  <matplotlib.axis.XTick at 0x210d7513340>,\n",
       "  <matplotlib.axis.XTick at 0x210d750d3d0>,\n",
       "  <matplotlib.axis.XTick at 0x210d7501340>,\n",
       "  <matplotlib.axis.XTick at 0x210d75175b0>,\n",
       "  <matplotlib.axis.XTick at 0x210d7517ac0>,\n",
       "  <matplotlib.axis.XTick at 0x210d7517fd0>],\n",
       " [Text(0, 0, 'mean radius'),\n",
       "  Text(0, 0, 'mean texture'),\n",
       "  Text(0, 0, 'mean perimeter'),\n",
       "  Text(0, 0, 'mean area'),\n",
       "  Text(0, 0, 'mean smoothness'),\n",
       "  Text(0, 0, 'mean compactness'),\n",
       "  Text(0, 0, 'mean concavity'),\n",
       "  Text(0, 0, 'mean concave points'),\n",
       "  Text(0, 0, 'mean symmetry'),\n",
       "  Text(0, 0, 'mean fractal dimension'),\n",
       "  Text(0, 0, 'radius error'),\n",
       "  Text(0, 0, 'texture error'),\n",
       "  Text(0, 0, 'perimeter error'),\n",
       "  Text(0, 0, 'area error'),\n",
       "  Text(0, 0, 'smoothness error'),\n",
       "  Text(0, 0, 'compactness error'),\n",
       "  Text(0, 0, 'concavity error'),\n",
       "  Text(0, 0, 'concave points error'),\n",
       "  Text(0, 0, 'symmetry error'),\n",
       "  Text(0, 0, 'fractal dimension error'),\n",
       "  Text(0, 0, 'worst radius'),\n",
       "  Text(0, 0, 'worst texture'),\n",
       "  Text(0, 0, 'worst perimeter'),\n",
       "  Text(0, 0, 'worst area'),\n",
       "  Text(0, 0, 'worst smoothness'),\n",
       "  Text(0, 0, 'worst compactness'),\n",
       "  Text(0, 0, 'worst concavity'),\n",
       "  Text(0, 0, 'worst concave points'),\n",
       "  Text(0, 0, 'worst symmetry'),\n",
       "  Text(0, 0, 'worst fractal dimension')])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 8))\n",
    "plt.boxplot(data1)\n",
    "plt.xticks(np.arange(30)+1, cancer.feature_names, rotation=90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "scores =  0.965034965034965\n"
     ]
    }
   ],
   "source": [
    "# LogisticRegression에 표준화 적용\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(data1, \n",
    "                                                    cancer.target,\n",
    "                                                   random_state=0)\n",
    "model = LogisticRegression(max_iter=10000).fit(X_train, y_train)\n",
    "scores = model.score(X_test, y_test)\n",
    "\n",
    "print(\"scores = \", scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "scores =  [0.9440559440559441, 0.9440559440559441, 0.9440559440559441, 0.9440559440559441, 0.9440559440559441, 0.9440559440559441, 0.9440559440559441, 0.9440559440559441, 0.9440559440559441, 0.9440559440559441]\n",
      "0.944055944055944\n"
     ]
    }
   ],
   "source": [
    "# LinearSVC에 표준화 적용\n",
    "\n",
    "scores = []\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(data1, cancer.target,\n",
    "                                                   random_state=0)\n",
    "\n",
    "for i in range(10):\n",
    "    model = LinearSVC(max_iter=10000).fit(X_train, y_train)\n",
    "    scores.append(model.score(X_test, y_test))\n",
    "\n",
    "print(\"scores = \", scores)\n",
    "print(np.mean(np.array(scores)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "scores =  0.951048951048951\n"
     ]
    }
   ],
   "source": [
    "# KNN에 표준화 적용\n",
    "\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(data1, cancer.target,\n",
    "                                                   random_state=0)\n",
    "\n",
    "model = KNeighborsClassifier(n_neighbors=5).fit(X_train, y_train)\n",
    "scores = model.score(X_test, y_test)\n",
    "\n",
    "print(\"scores = \", scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "scores =  0.965034965034965\n"
     ]
    }
   ],
   "source": [
    "# C값 조정\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(data1, cancer.target, random_state=0)\n",
    "model = LogisticRegression(C=1, max_iter=10000).fit(X_train, y_train)\n",
    "scores = model.score(X_test, y_test)\n",
    "\n",
    "print(\"scores = \", scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "scores =  [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]\n",
      "1.0\n"
     ]
    }
   ],
   "source": [
    "# C값 조정\n",
    "\n",
    "scores = []\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(data1, cancer.target)\n",
    "\n",
    "for i in range(10):\n",
    "    model = LinearSVC(C=0.01, max_iter=10000).fit(X_train, y_train)\n",
    "    scores.append(model.score(X_test, y_test))\n",
    "\n",
    "print(\"scores = \", scores)\n",
    "print(np.mean(np.array(scores)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 다중 클래스 분류"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x210d7764b80>,\n",
       " <matplotlib.lines.Line2D at 0x210d7764ee0>,\n",
       " <matplotlib.lines.Line2D at 0x210d77702b0>]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.datasets import make_blobs\n",
    "#from sklearn.svm import LinearSVC\n",
    "\n",
    "X, y = make_blobs(random_state=42)\n",
    "mglearn.discrete_scatter(X[:, 0], X[:, 1], y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-0.1749234   0.23140858]\n",
      " [ 0.47621748 -0.06936565]\n",
      " [-0.18914141 -0.20400449]]\n",
      "[-1.07745376  0.13140521 -0.08604984]\n"
     ]
    }
   ],
   "source": [
    "linear_svc = LinearSVC().fit(X, y)\n",
    "#print(linear_svc.coef_)\n",
    "#print(linear_svc.icoef_[0])\n",
    "#print(linear_svc.icoef_[1])\n",
    "#print(linear_svc.intercept_)\n",
    "\n",
    "#print(linear_svc.coef_.shape)\n",
    "#print(linear_svc.intercept_.shape)\n",
    "print(linear_svc.coef_)\n",
    "print(linear_svc.intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 산포도\n",
    "mglearn.plots.plot_2d_classification(linear_svc, X, fill=True, alpha=.7)\n",
    "mglearn.discrete_scatter(X[:, 0], X[:, 1], y)\n",
    "line = np.linspace(-15, 15)\n",
    "\n",
    "for coef, intercept, color in zip(linear_svc.coef_, linear_svc.intercept_, mglearn.cm3.colors):\n",
    "    plt.plot(line, -(line*coef[0] + intercept)/coef[1], c=color)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "#### 적합도 검정(성능 측정)p.135                                                                                                                                                                                                                                                                 \n",
    "\n",
    "- Accuracy : 정확도(모든 데이터들 중에서)\n",
    "    - (TN + TP) / (TN + FP + FN + TP)\n",
    "    \n",
    "    \n",
    "- Precision : 정밀도(예측한 양성들 중에서 실제값이 정말로 양성과 같은 데이터의 비율)\n",
    "    - TP / (FP + TP)\n",
    "    \n",
    "    \n",
    "- Recall : 재현율(실제 값이 양성들 중에서 예측과 실제값이 양성으로 일치한 비율)\n",
    "    - TP / (FN + TP)\n",
    "    \n",
    "- F1 Score(p.136)\n",
    "    - 정밀도와 재현율의 조화 평균\n",
    "    \n",
    "- ROC 그래프와 AUC 곡선 아래의 면적(p.141)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x210d6ba56d0>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "arr = [[5, 0, 0, 0],\n",
    "      [0, 10, 0, 0],\n",
    "      [0, 0, 15, 0],\n",
    "      [0, 0, 0, 5]]\n",
    "\n",
    "df_cm = pd.DataFrame(arr, index=[i for i in \"ABCD\"], columns=[i for i in \"ABCD\"])\n",
    "df_cm\n",
    "\n",
    "#히트맵\n",
    "plt.figure(figsize=(5, 3))\n",
    "sns.heatmap(df_cm, annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x210d6b6ac70>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "arr = [[9, 1, 0, 0],\n",
    "      [1, 15, 3, 1],\n",
    "      [5, 0, 24, 1],\n",
    "      [0, 4, 1, 15]]\n",
    "\n",
    "df_cm = pd.DataFrame(arr, index=[i for i in \"ABCD\"], columns=[i for i in \"ABCD\"])\n",
    "df_cm\n",
    "\n",
    "#히트맵\n",
    "plt.figure(figsize=(5, 3))\n",
    "sns.heatmap(df_cm, annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x210d6ceb580>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 정규화\n",
    "\n",
    "total = np.sum(arr, axis=1)\n",
    "total\n",
    "arr = arr / total[:, None]\n",
    "arr\n",
    "\n",
    "df_cm = pd.DataFrame(arr, index=[i for i in \"ABCD\"], columns=[i for i in \"ABCD\"])\n",
    "df_cm\n",
    "plt.figure(figsize=(5, 3))\n",
    "sns.heatmap(df_cm, annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2, 0, 0],\n",
       "       [0, 0, 1],\n",
       "       [1, 0, 2]], dtype=int64)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 다중 분류\n",
    "y_true = [2, 0, 2, 2, 0, 1]\n",
    "y_pred = [0, 0, 2, 2, 0, 2]\n",
    "\n",
    "confusion_matrix(y_true, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3, 1],\n",
       "       [0, 2]], dtype=int64)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 이진 분류\n",
    "y_true = [1, 0, 1, 1, 0, 1]\n",
    "y_pred = [0, 0, 1, 1, 0, 1]\n",
    "\n",
    "confusion_matrix(y_true, y_pred, labels=[1, 0])  # 행의 순서 바꾸기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "     class 0       0.67      1.00      0.80         2\n",
      "     class 1       1.00      0.75      0.86         4\n",
      "\n",
      "    accuracy                           0.83         6\n",
      "   macro avg       0.83      0.88      0.83         6\n",
      "weighted avg       0.89      0.83      0.84         6\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "\n",
    "y_true = [1, 0, 1, 1, 0, 1] # 실제값\n",
    "y_pred = [0, 0, 1, 1, 0, 1] # 예측값\n",
    "\n",
    "print(classification_report(y_true, y_pred, target_names=[\"class 0\", \"class 1\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ROC curve(with AUC)\n",
    "\n",
    "- 이진 분류에서 사용"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import make_classification\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 1, 1, 1, 1, 0, 1, 1, 0])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 샘플 불러오기\n",
    "X, y = make_classification(n_samples=1000, n_features=2, n_informative=2, n_redundant=0, \n",
    "                           random_state=0)\n",
    "X[:10, :]\n",
    "y[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#훈련\n",
    "model = LogisticRegression().fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 일 시키기(원래는 새로운 데이터를 넣어야함)\n",
    "y_hat = model.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 1 1 1 1 0 1 1 0 1 1 1 0 0 0 1 1 1 1 0 0 0 0 1 0 0 1 0 1 0 1 1 1 1 0 1\n",
      " 0 0 1]\n",
      "[0 1 1 1 1 1 0 1 1 0 1 1 1 0 0 0 1 1 1 1 0 0 0 0 0 0 0 1 0 1 0 1 1 1 1 0 1\n",
      " 0 0 1]\n"
     ]
    }
   ],
   "source": [
    "print(y_hat[:40])\n",
    "print(y[:40])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[477,  20],\n",
       "       [ 28, 475]], dtype=int64)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 오차표 작성\n",
    "confusion_matrix(y, y_hat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "      class0       0.94      0.96      0.95       497\n",
      "      class1       0.96      0.94      0.95       503\n",
      "\n",
      "    accuracy                           0.95      1000\n",
      "   macro avg       0.95      0.95      0.95      1000\n",
      "weighted avg       0.95      0.95      0.95      1000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y, y_hat, target_names=[\"class0\", \"class1\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ROC 그래프\n",
    "from sklearn.metrics import roc_curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'True Positive Rate(Recall)')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#X는 원래 테스트했던 데이터를 넣어줘야함\n",
    "fpr, tpr, thresholds = roc_curve(y, model.decision_function(X)) \n",
    "\n",
    "plt.plot(fpr, tpr, \"o-\", label=\"Logistic Regression\")\n",
    "\n",
    "# 기준선 추가\n",
    "plt.plot([0, 1], [0, 1], \"k--\", label=\"Random Guess\")\n",
    "\n",
    "plt.xlabel(\"False Positive Rate(fall-out)\")\n",
    "plt.ylabel(\"True Positive Rate(Recall)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9795592641335089"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# AUC 면적 구하기 p.143\n",
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "roc_auc_score(y, model.decision_function(X))\n",
    "# 0.5가 최하, 결과는 0.9"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "### 사례1 : 타이타닉 예제\n",
    "\n",
    "- 성능을 제대로 측정했는지에 대한 관점"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"data/train.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 전처리가 필요없는 컬럼\n",
    "cols_to_keep = [\"Survived\", \"Age\", \"Fare\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>Pclass_1</th>\n",
       "      <th>Pclass_2</th>\n",
       "      <th>Pclass_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pclass_1  Pclass_2  Pclass_3\n",
       "0         0         0         1\n",
       "1         1         0         0\n",
       "2         0         0         1\n",
       "3         1         0         0\n",
       "4         0         0         1"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pclass 1, 2, 3 -> 더미변수 : one hot encoding\n",
    "# Pclass의 더미변수화(one hot encoding)\n",
    "\n",
    "dummy_class = pd.get_dummies(df[\"Pclass\"], prefix=\"Pclass\")\n",
    "dummy_class.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
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       "        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>Sex_female</th>\n",
       "      <th>Sex_male</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Sex_female  Sex_male\n",
       "0           0         1\n",
       "1           1         0\n",
       "2           1         0\n",
       "3           1         0\n",
       "4           0         1"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sex의 더미변수화\n",
    "dummy_sex = pd.get_dummies(df[\"Sex\"], prefix=\"Sex\")\n",
    "dummy_sex.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Pclass_1</th>\n",
       "      <th>Pclass_2</th>\n",
       "      <th>Pclass_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived   Age     Fare  Pclass_1  Pclass_2  Pclass_3\n",
       "0         0  22.0   7.2500         0         0         1\n",
       "1         1  38.0  71.2833         1         0         0\n",
       "2         1  26.0   7.9250         0         0         1\n",
       "3         1  35.0  53.1000         1         0         0\n",
       "4         0  35.0   8.0500         0         0         1"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 데이터 합치기1\n",
    "df1 = df[cols_to_keep].join(dummy_class.loc[:, \"Pclass1\":])\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Pclass_1</th>\n",
       "      <th>Pclass_2</th>\n",
       "      <th>Pclass_3</th>\n",
       "      <th>Sex_male</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived   Age     Fare  Pclass_1  Pclass_2  Pclass_3  Sex_male\n",
       "0         0  22.0   7.2500         0         0         1         1\n",
       "1         1  38.0  71.2833         1         0         0         0\n",
       "2         1  26.0   7.9250         0         0         1         0\n",
       "3         1  35.0  53.1000         1         0         0         0\n",
       "4         0  35.0   8.0500         0         0         1         1"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 데이터 합치기2\n",
    "df1 = df1.join(dummy_sex.loc[:, \"Sex_male\"])\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Age', 'Fare', 'Pclass_1', 'Pclass_2', 'Pclass_3', 'Sex_male'], dtype='object')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Survied를 제외한 나머지 컬럼(특성) 추출\n",
    "train_cols = df1.columns[1:]\n",
    "train_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 결측치 확인\n",
    "np.isnan(df1[train_cols])\n",
    "df1.isnull().sum()\n",
    "\n",
    "# 결측치 삭제 \n",
    "#df1[train_cols] = df1[train_cols].dropna(axis=0)\n",
    "#df1[\"Survived\"] = df1[\"Survived\"].dropna(axis=0).\n",
    "\n",
    "# 결측치 0으로 대체\n",
    "df1[train_cols] = df1[train_cols].fillna(0)\n",
    "df1[\"Survived\"] = df1[\"Survived\"].fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 통계정보확인\n",
    "import statsmodels.api as sm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.458488\n",
      "         Iterations 6\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "        <td>Model:</td>              <td>Logit</td>      <td>Pseudo R-squared:</td>    <td>0.311</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Dependent Variable:</td>     <td>Survived</td>           <td>AIC:</td>         <td>829.0250</td> \n",
       "</tr>\n",
       "<tr>\n",
       "         <td>Date:</td>        <td>2021-01-12 11:44</td>       <td>BIC:</td>         <td>857.7791</td> \n",
       "</tr>\n",
       "<tr>\n",
       "   <td>No. Observations:</td>         <td>891</td>        <td>Log-Likelihood:</td>    <td>-408.51</td> \n",
       "</tr>\n",
       "<tr>\n",
       "       <td>Df Model:</td>              <td>5</td>            <td>LL-Null:</td>        <td>-593.33</td> \n",
       "</tr>\n",
       "<tr>\n",
       "     <td>Df Residuals:</td>           <td>885</td>         <td>LLR p-value:</td>    <td>1.0371e-77</td>\n",
       "</tr>\n",
       "<tr>\n",
       "      <td>Converged:</td>           <td>1.0000</td>           <td>Scale:</td>         <td>1.0000</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "    <td>No. Iterations:</td>        <td>6.0000</td>              <td></td>               <td></td>     \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>      <th>Coef.</th>  <th>Std.Err.</th>     <th>z</th>     <th>P>|z|</th> <th>[0.025</th>  <th>0.975]</th> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Age</th>      <td>-0.0159</td>  <td>0.0053</td>   <td>-2.9843</td> <td>0.0028</td> <td>-0.0264</td> <td>-0.0055</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Fare</th>     <td>0.0015</td>   <td>0.0021</td>   <td>0.7370</td>  <td>0.4611</td> <td>-0.0026</td> <td>0.0056</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Pclass_1</th> <td>2.6932</td>   <td>0.3372</td>   <td>7.9875</td>  <td>0.0000</td> <td>2.0323</td>  <td>3.3540</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Pclass_2</th> <td>1.8519</td>   <td>0.2670</td>   <td>6.9358</td>  <td>0.0000</td> <td>1.3286</td>  <td>2.3752</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Pclass_3</th> <td>0.6215</td>   <td>0.1808</td>   <td>3.4377</td>  <td>0.0006</td> <td>0.2672</td>  <td>0.9758</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sex_male</th> <td>-2.6151</td>  <td>0.1860</td>  <td>-14.0594</td> <td>0.0000</td> <td>-2.9797</td> <td>-2.2505</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary2.Summary'>\n",
       "\"\"\"\n",
       "                         Results: Logit\n",
       "=================================================================\n",
       "Model:              Logit            Pseudo R-squared: 0.311     \n",
       "Dependent Variable: Survived         AIC:              829.0250  \n",
       "Date:               2021-01-12 11:44 BIC:              857.7791  \n",
       "No. Observations:   891              Log-Likelihood:   -408.51   \n",
       "Df Model:           5                LL-Null:          -593.33   \n",
       "Df Residuals:       885              LLR p-value:      1.0371e-77\n",
       "Converged:          1.0000           Scale:            1.0000    \n",
       "No. Iterations:     6.0000                                       \n",
       "------------------------------------------------------------------\n",
       "              Coef.   Std.Err.     z      P>|z|    [0.025   0.975]\n",
       "------------------------------------------------------------------\n",
       "Age          -0.0159    0.0053   -2.9843  0.0028  -0.0264  -0.0055\n",
       "Fare          0.0015    0.0021    0.7370  0.4611  -0.0026   0.0056\n",
       "Pclass_1      2.6932    0.3372    7.9875  0.0000   2.0323   3.3540\n",
       "Pclass_2      1.8519    0.2670    6.9358  0.0000   1.3286   2.3752\n",
       "Pclass_3      0.6215    0.1808    3.4377  0.0006   0.2672   0.9758\n",
       "Sex_male     -2.6151    0.1860  -14.0594  0.0000  -2.9797  -2.2505\n",
       "=================================================================\n",
       "\n",
       "\"\"\""
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = sm.Logit(df1[\"Survived\"], df1[train_cols]).fit()\n",
    "model.summary2()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 머신러닝\n",
    "model2 = LogisticRegression().fit(df1[train_cols], df1[\"Survived\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>Age</th>\n",
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       "      <th>Pclass_1</th>\n",
       "      <th>Pclass_2</th>\n",
       "      <th>Pclass_3</th>\n",
       "      <th>Sex_male</th>\n",
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       "      <th>0</th>\n",
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       "      <th>1</th>\n",
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       "      <td>1</td>\n",
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       "      <th>2</th>\n",
       "      <td>1</td>\n",
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       "      <td>7.9250</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.4583</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>51.8625</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>21.0750</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>27.0</td>\n",
       "      <td>11.1333</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>14.0</td>\n",
       "      <td>30.0708</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>16.7000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1</td>\n",
       "      <td>58.0</td>\n",
       "      <td>26.5500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>31.2750</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>7.8542</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1</td>\n",
       "      <td>55.0</td>\n",
       "      <td>16.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>29.1250</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>18.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1</td>\n",
       "      <td>34.0</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1</td>\n",
       "      <td>15.0</td>\n",
       "      <td>8.0292</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1</td>\n",
       "      <td>28.0</td>\n",
       "      <td>35.5000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>21.0750</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>31.3875</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>263.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.8792</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>27.7208</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>146.5208</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>10.5000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>82.1708</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>52.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.2292</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>18.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>1</td>\n",
       "      <td>14.0</td>\n",
       "      <td>11.2417</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Survived   Age      Fare  Pclass_1  Pclass_2  Pclass_3  Sex_male  predict\n",
       "0          0  22.0    7.2500         0         0         1         1        0\n",
       "1          1  38.0   71.2833         1         0         0         0        1\n",
       "2          1  26.0    7.9250         0         0         1         0        1\n",
       "3          1  35.0   53.1000         1         0         0         0        1\n",
       "4          0  35.0    8.0500         0         0         1         1        0\n",
       "5          0   0.0    8.4583         0         0         1         1        0\n",
       "6          0  54.0   51.8625         1         0         0         1        0\n",
       "7          0   2.0   21.0750         0         0         1         1        0\n",
       "8          1  27.0   11.1333         0         0         1         0        1\n",
       "9          1  14.0   30.0708         0         1         0         0        1\n",
       "10         1   4.0   16.7000         0         0         1         0        1\n",
       "11         1  58.0   26.5500         1         0         0         0        1\n",
       "12         0  20.0    8.0500         0         0         1         1        0\n",
       "13         0  39.0   31.2750         0         0         1         1        0\n",
       "14         0  14.0    7.8542         0         0         1         0        1\n",
       "15         1  55.0   16.0000         0         1         0         0        1\n",
       "16         0   2.0   29.1250         0         0         1         1        0\n",
       "17         1   0.0   13.0000         0         1         0         1        0\n",
       "18         0  31.0   18.0000         0         0         1         0        1\n",
       "19         1   0.0    7.2250         0         0         1         0        1\n",
       "20         0  35.0   26.0000         0         1         0         1        0\n",
       "21         1  34.0   13.0000         0         1         0         1        0\n",
       "22         1  15.0    8.0292         0         0         1         0        1\n",
       "23         1  28.0   35.5000         1         0         0         1        0\n",
       "24         0   8.0   21.0750         0         0         1         0        1\n",
       "25         1  38.0   31.3875         0         0         1         0        1\n",
       "26         0   0.0    7.2250         0         0         1         1        0\n",
       "27         0  19.0  263.0000         1         0         0         1        1\n",
       "28         1   0.0    7.8792         0         0         1         0        1\n",
       "29         0   0.0    7.8958         0         0         1         1        0\n",
       "30         0  40.0   27.7208         1         0         0         1        0\n",
       "31         1   0.0  146.5208         1         0         0         0        1\n",
       "32         1   0.0    7.7500         0         0         1         0        1\n",
       "33         0  66.0   10.5000         0         1         0         1        0\n",
       "34         0  28.0   82.1708         1         0         0         1        0\n",
       "35         0  42.0   52.0000         1         0         0         1        0\n",
       "36         1   0.0    7.2292         0         0         1         1        0\n",
       "37         0  21.0    8.0500         0         0         1         1        0\n",
       "38         0  18.0   18.0000         0         0         1         0        1\n",
       "39         1  14.0   11.2417         0         0         1         0        1"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1[\"predict\"] = model2.predict(df1[train_cols])\n",
    "df1.head(40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[242, 100],\n",
       "       [ 93, 456]], dtype=int64)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 오차표 작성\n",
    "confusion_matrix(df1[\"Survived\"], df1[\"predict\"], labels=[1, 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "정확도 :  0.7833894500561167\n",
      "정확도 :  0.7833894500561167\n"
     ]
    }
   ],
   "source": [
    "# 일단 정확도를 확인\n",
    "print(\"정확도 : \", model2.score(df1[train_cols], df1[\"Survived\"]))\n",
    "print(\"정확도 : \", (242 + 456) / (242 + 100 + 93 + 456))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.82      0.83      0.83       549\n",
      "           1       0.72      0.71      0.71       342\n",
      "\n",
      "    accuracy                           0.78       891\n",
      "   macro avg       0.77      0.77      0.77       891\n",
      "weighted avg       0.78      0.78      0.78       891\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(df1[\"Survived\"], df1[\"predict\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'True Positive Rate(Recall)')"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Kxt8RPDFRsDnpqiBEZMzfli1bRv/+/dm5cydDhgxh5MiRVKhQwe2wIpavm7sLKGAtbFW91ZGITJH5O4InCph0YyvnAzImB1Vl4sSJVK9enbfeeouEhAS3Q4p4vrp6hgUlClMik97d7Ndxk29uZd07JihUlTfffJOLL744V1G1smXLuh2awUeRNlXdpqrb8My8rQOcmePLhICU1DS/yjDUjYu1pG+CYvv27XTu3Jnu3bszbdo0wDN6x5J+6PB35u5SPEXXGgKHgH3Aew7FZPyUkprGQD/G60dHiVXWNI7Lyspi9uzZDBkyhMzMTJ599lnuv/9+t8My+fA38VdW1c4iMl5Vh4vIQkejMifJLrKWc0btw4vW+3ydjeAxwTJu3DhGjBjBpZdeyqxZs2jUqJHbIZkC+Jv400WkDFBZRGoBVrzFQSmpaYx8eyP70vNfDSttX7pfLX2wETzGWcePH2fPnj3UrFmTfv36UbduXe666y4ruRDi/E389wLlgXnAbGC8YxFFuEAUVssW62NFLWNK4uuvv6ZXr16UKVOGNWvWEB8fz9132wjw0sDfzFBfVQ+p6v+A64HvHYwpIqWkpnH6o8sDlvQBxl9/TsDey5hsR48e5YknnuC8887jl19+YdCgQdbCL2X8bfEn4Vk4HVU9LiJJeJZXNAGQkprGoDe/ogQl808yxYZuGgds2bKFa6+9lk2bNnH77bfz7LPPUr16dbfDMkXkb4s/75/zyoEOJJKNWrbRkr4pFU499VROPfVUli9fzvz58y3pl1L+Jv5kEXlVRLqIyPN4VtgyAfB4ygb2Hsn/Jm5hYqKExMbVcv1Frlg22pK+Cbj//ve/XH755Rw+fJjY2Fjef/99OnXq5HZYpgT86upR1edEpB3QBliuqsucDSv8+Rq5k59oETJVqWsLpJgg2LdvHw8//DBz587l9NNPZ/v27VZULUz4lfi9QzkbAFnAChGppaq/OxpZGCrOguc2Dt+44a233uKee+7hjz/+YOjQoTz55JPExsa6HZYJEH9v7r4CrARuBKYA/wK6ORVUOCrOMM3YmCi+HWPj8E1wqSpPP/00NWvWZNmyZZx33nluh2QCzN/EH6+qM0XkJlVVEYlzNKowUpwunWw2HNMEi6ry2muvcdlll1G7du0TRdViYmLcDs04wN+bu4dEpCWgIlLfyYDCSUpqGsOXbChW0u9xQX3r3jFB8csvv3D11Vdz++23M336dABq1aplST+M+dvi7wdMAqoBT3ufGx8mvbuZ9AzfK2LlZYuem2DIysri+eefZ+jQoagqU6dO5d5773U7LBME/o7q+R24A0BEyuNZkvFJB+Mq1YpzExegaoUYnry2ubX0TVCMHTuWJ554gssvv5xZs2bRsGFDt0MyQeJrBa4ewMN4yjD3AM4GRgMvOh9a6ZSSmsbgReuLtOC5Dc80wXL8+HF2795N7dq16d+/Pw0aNOD222+3kgsRxleL/348Y/drAUvw1OjpqKp7nA6stHps6Qa/k7516ZhgWr9+PT179iQmJoa1a9cSHx/PHXfc4XZYxgW+bu4eVtVjqvorUBa4w5J+4Q4f869Pf8rNrSzpm6D466+/ePzxx0lISCAtLY0hQ4YQFWWVWyOZrxZ/YxEZh6dWT1UgKfsjoao+6nBsYcuWQTTB8tNPP3HNNdfw3XffceeddzJ58mSqVavmdljGZb4Sf87PgSucDKQ0K8rN3NiYaFsG0QRNnTp1qFevHlOmTKFjx45uh2NChK/EX1NVF+W3wzuJ6xxVXRn4sEqP7LH6/gzbtJu4Jhjee+89JkyYwLJly6hYsSLvvWfLY5vcfCX+MiLyKvA+8CVwBKiDpxZ/TeAxZ8MLfaOWbfR7rP6aYZc4HI2JZHv27GHw4MG8/PLLnHnmmfz222+cfvrpbodlQlChiV9VF4hIMp5FWC4GKgA7gRdVdUsQ4gtpRSmpHG3D5YyDFi9ezIABA9i9ezePPvooI0aMoHz58m6HZUKUzwlcqpoB/FdEfgDqquo658MKfUUtutb9/HoORmMimary3HPPUadOHVasWEGrVq3cDsmEOL/GdInIo8BEYIaIlPcuxuLrNWNE5GMRWSMizfPsu1tEPvHuu7R4obsnJTWtSEk/sXE1G7ppAkpVmT9/Pjt27EBESE5O5tNPP7Wkb/zi72DeK1S1O7BfVf8CGhV2sHfRllqq2p6/6/xk72sOtAP+qaqJqvrf4oXunknvbvbruLpxsUy5uRWv9WnrcEQmkmzdupUrr7ySO++8k5kzZwJQs2ZNK6pm/OZvkTYVkUre72XwvebuFcACAFX9RkRyDhzuBWwDPhCRP4B7VXV3zheLSF+gL0D9+qFVDDQlNc2vYZuJjatZwjcBlZWVxYwZMxg+fDgiwvTp07nnnnvcDsuUQv62+IfjGcffAvgvMNbH8TWBXTmeHxeR7HOdDuxW1Q7AIvIp9qaqs1Q1QVUT4uPj/QzReZdP/oiBb37l87jTa1a0pG8CLikpiQceeIALL7yQb775hgEDBtgMXFMs/rb496nqhSISD+wGmvg4fj+emb7ZslQ1y/v4OPCO9/G/gf7+Bhts2ROzftuXTpkoyMjy/Rqrv2MCKSMjg927d3Pqqadyzz330KhRI2677TYrqmZKxN/mwr8AVHWXqiqepRgLswq4AUBEmgHbc+xbB3TyPu4AfO1vsMGUPTErbV86in9JH7CkbwLmyy+/5B//+AddunQhKyuL+Ph4evToYUnflFihiV9E2ovIOuA8EVkrIutE5DNgjY/3XQ6UFZFVeBZuGSoiE0WkLJ4/Ih1E5CM8rf2kEl+FA4oyMStb3ThbjNqUXHp6OsOGDaNNmzb8/vvvDB8+3Lp0TED5msD1MdBWRKaq6gP+vqm3WyfvXaeh3u/H8CzaHrJSUtP8npiVzWrwmED48ccfufrqq/n+++/p1asXkyZNomrVqr5faEwR+NvHP0hE2pJjNI+qhm0BkFHLNhbpeFs5ywTK//t//49GjRoxY8YMLrvsMrfDMWHK38S/BM8onYbAITwrcoVl4i9qa99u5pqSWrFiBRMmTODf//43lSpV4j//+Y/bIZkw52/HYWVV7QV8pqrXAWHbme2rtR8tgvD35CxL+qa4/vzzT+68806uuuoq/vjjD3bs2OF2SCZC+NviT8+euCUitYDmvl5QGvnT2n/mppbWpWNKRFVJTk7mvvvuY8+ePYwYMYLHHnuMcuXKuR2aiRD+Jv57gfLAPGA2MN6xiFzkTykGS/omEKZPn069evV47733aNmypdvhmAjjV1ePqm5V1UOq+j9V7QyEZVEQX6UYEhvbknWmeFSVl156id9+++1EUbVPPvnEkr5xha9x/Jd7x+8vEZE4EWnpHX9/VnDCC67CaubHRGFlGEyxbNmyhcsvv5yePXvy/POewrbx8fGUKePvB25jAsvXv7zRwFVAAzxF16KBvqr6vdOBuSFTtcB9k260cremaDIzM5k2bRqPPfYY0dHRzJw5k759+7odljE+E3+6qu4D9olIA6C1qh4NQlyuqFohJt+bu3GxMda3b4osKSmJkSNHcvXVVzNz5kzq1bPFeExo8JX463pLJAue1v6d2XVCVHWWw7EFVUpqGvvTT076MdHCyM5hOYjJOODYsWPs3r2bOnXqMGDAAM444wxuueUWq69jQoqvm7vjgKPAXzkeZ3+FlVHLNpKVT09PTJRYa9/45fPPP89VVK1GjRp0797dkr4JOb5q9cwLViBuKmz8/hF/y3KaiJWens6TTz7JM888Q+3atZk5c6YVVTMhLaKHFaSkpjHy7Y3sy6eLxxh//PDDD3Tq1Ikff/yRPn368NRTTxEXF+d2WMYUKmITf0pqGkOS15ORWfBIHvDc3DCmIPXq1eOMM87ghRde4JJLLnE7HGP8ErGfR0ct2+gz6QP4PsJEmuXLl9OuXTsOHTpE+fLlWb58uSV9U6r4lfi9k7ceF5FnRKScd1WtUs3fCpy2uIrJtnv3bnr06ME111zD3r172blzp9shGVMs/rb45wNfAP/wjuMPy1o9ecVEiy2uYlBV3njjDZo2bcrChQsZOXIkX375JU2a+Fp62pjQ5G8ffwVV/Y+IDPE+r1zo0SGoqDdyK5aNZmzXs20opwHghRdeoFGjRsydO5cWLVq4HY4xJeJv4v9dRDoD0SKSCBRezSzEpKSmMWjhV/mO08/PlJtbWcKPcKrK3Llz6dSpE3Xq1GHRokVUrVqV6Ohot0MzpsT87erpC/wDz+pb3YC7nArICQVNziqIJf3I9tNPP3HppZfSp08fZs3yTFCvUaOGJX0TNvxt8T8HzFTVEU4G45SiLKVYWIVOE94yMzN57rnnePzxx4mJiWHWrFn07t3b7bCMCTh/W/zTgNtFZLmI3CUiYbtUUPfzrZBWpEpKSmLw4MFcdtllbNq0iT59+li5BROW/Grxq+p6YKB3+cXhwI9A2GXIxMbVbA3dCHPs2DF27dpF3bp1ue+++zjrrLO46aabLOGbsOZX4heROsBteGrzrwc6ORlUMAlQJy6WIR3PtL79CPPZZ5/Rs2dPYmNj+fTTT6levTo333yz22EZ4zh/+/jnAi8CV6rqMQfjCbqfJ1ztdggmyI4cOcKIESOYMmUKderUYeLEiVZUzUSUQhO/iNRW1Z3AQDzVCxrmqMdfKlbhSklNK3BflH2ajzg//PADV155JVu2bKF///5MnDiRU045xe2wjAkqXy3+W4ApwDA8iT87VSrQ08G4AmbUso0F7itXxlp5kUJVERHq1atH8+bNefHFF2nfvr3bYRnjCl/1+Kd4Hz6lqt9mbxeR0x2NKoAKG8r5l9XajwjLli1jwoQJrFixgsqVK/P222+7HZIxrvK3yTsjz/NXAh2IG+pYAbawtmvXLrp3707nzp05ePAgf/zxh9shGRMSCk38ItJeRNYB54nIWhFZJyKfAWuCE56zrABbeFJVXn/9dZo2bcrixYsZPXo0n3/+OY0bN3Y7NGNCgq+uno+BtiIyVVUfCFJMARUXG5NvYbbYmCgbvhnGZs+ezemnn86cOXNo3ry52+EYE1J8tfhrex9OF5Ezcn75emMRGSMiH4vIGhE56X+eiNQSkSMiUr6YsftlZOfmxOQZvhMTJYy//hwnT2uCLCsrixdeeIG0tDREhOTkZFavXm1J35h8+Orjv8X7fXier2GFvUhE2gG1VLU90A+YlM9hw4DdRYq2GLq0rsukG1tSNtpzqXXjYpl0Y0tr7YeRH374gYsvvpj+/fszZ84cAKpXr25F1YwpgF+jelT17iK+7xXAAu9rvxGRajl3isi5eIaEbini+xZLl9Z1ef2zX4gSeKNv22Cc0gTB8ePHmTx5Mk8++STlypVj7ty53H13Uf+pGhN5/F16cZb3+/ki8pWIPOHjJTWBXTmeHxeRKO97VAAmAKMKOV9fEflcRD7ftWtXQYeZCDdmzBiGDh3KlVdeyaZNm+jZs6fV2DHGD/4O58we/nILcC5wkY/j9wNVczzPUtXsQfPPAhNVdX9BL1bVWaqaoKoJ8fHxfoZYsJTUNL76ZR+fbNlD4oQPCp3Na0Lb0aNH2b59OwAPPPAAycnJLFmyhDp16rgcmTGlh7+J/5CIjAK2ehN4JR/HrwJuAPAuzL7d+7gmcB7QR0TeAJoBLxcjbr+lpKYxfMkGjmV6/u6k7Utn+JINlvxLoXXr1tG6dWu6du1KVlYW1atXp1u3btbKN6aI/E38d+BZbH2qdxTOSB/HLwfKisgq4GlgqIhMBPZ5W/K3qOotwCYcXs1r0rubSc/IzLUtPSOTSe9udvK0JoAOHz7MwIEDSUxM5NChQ4wePdqKqhlTAv5W5zwA1AemAqnAS4Ud7P1UcE+ezUPzOa6Dn+cvtt/25b88cEHbTWj5/vvv6dixI1u3bmXAgAGMHz+eypUrux2WMaWav82muUA57/c4PIXbSoW4CjFF2m5Cg6pnkeQGDRrQsmVLVq5cyfTp0y3pGxMA/ib++qr6jKp+paqTgVIzK+Zonm6ebFqExddNcC1dupS2bdty8OBBypUrR0pKCu3atXM7LGPChr+JP1q8d9C8wzIrOhdS4DyesoEjBVTgzK+Mg3HX77//zn2pL5MAABUuSURBVE033cT111/P0aNHsaG8xjjD38Q/D1gmIgOBt4BXnQspMFJS03jtk18K3B9tI0FChqoyf/58mjZtyltvvcXYsWP57LPPaNSokduhGROW/F1sfY6IrATOAYapasGrm4SISe9uprDenEzr6wkp8+bNo2nTpsydO5ezzjrL7XCMCWu+ll7sgGc45hE8M3H7qarj9XUCwdeonbpWi99V2UXVOnfuTN26dVm0aBFVqlSx+jrGBIGvrp6xwOWqehEwDnjK+ZACo7BFVmKixWrxu2jz5s20b9+ee++9l7lz5wJQrVo1S/rGBImvxH9MVfcCqOoXQAPnQwqMIR3PJDbm5ERSsWw0k26w6pxuOH78OBMmTKBly5Zs3LiRl19+mREjRrgdljERx1cffyMRGed9LECT7Oeq+qijkZVQl9Z1+XzbHl779BdUIUrg1vPrk9TlbLdDi1hjxoxh9OjRdOvWjenTp1O7dm3fLzLGBJyvxH9HnucrnAok0FJS01j8RdqJ8fpZCou/SCOhQTVr7QfRX3/9xa5du6hXrx4PPvggrVq1omvXrm6HZUxE82fpxVKpsBo9lviDY82aNfTq1YuKFSvyv//9j2rVqlnSNyYEhG2lq7QCRvUUtN0EzqFDh3jggQdo164df/31F+PGjbOiasaEEH+LtJU60SL5jtW3iVvO2rx5M1dccQW//vor999/P2PHjqVSJV9VvI0xwRS2ib+gCVo2ccsZqoqI0LBhQ8477zxef/11EhMT3Q7LGJMPf5debCAic0TkTREpLyLtnQ6spAqaoGUTtwJv8eLFXHDBBSeKqi1ZssSSvjEhzN+O1znAM0C8qv4FDHEupMDIbxx/bEy0TdwKoB07dtCtWzduuOEGMjIyrKiaMaWEv4k/SlW/zfE85Dttu7Suy/jrz6ZKrKfufu0q5Rl//dk2oicAVJWXX36ZZs2asXz5ciZMmGBF1YwpRfzt498sIg8AFUXkNmCngzEFTJfWdTnwVwZPvLWR5fdfSPVK5dwOKWzMnz+fFi1aMGfOHM480z5FGVOa+Nvivw84DHwO1ADudiwiE5KysrKYMWMG27dvR0RYvHgxH3/8sSV9Y0ohf8syZ+FZdnGus+GYUPTtt9/Su3dv1q5dy549exgxYgRVq1Z1OyxjTDH5lfhFZB2cKG/fENinqs2cCsqEhoyMDJ566ilGjx5NpUqVmD9/Pj169HA7LGNMCfnb4m+b/VhEYoBhjkVkQsaYMWMYM2YMN910E1OnTqVWrVpuh2SMCYAiT+BS1QwRqexEMIH2eMoGXvvUs/ziP8a+b9U5/ZCens6uXbuoX78+AwcOJCEhgc6dO7sdljEmgPzt6hnP3109dYFjjkUUII+nbODVHGvuZiknnlvyz9+qVavo3bs3lSpVOlFUzZK+MeHH31E9K4B3vd+fUtU+zoUUGAs+/bVI2yPZgQMHGDBgABdddNGJfn0rqmZM+PK3q2eIql7jaCQBZrV6/PPdd99xxRVXsH37dgYOHEhSUhIVK1Z0OyxjjIP8TfyrROQyYC1wHEBVQ7q7x6pzFi67qNppp51GmzZtePPNN2nbtq3vFxpjSr1CP8+LSHXvwyuBx4Dl/N3lE9K6n1+vSNsjhaqyaNEi2rRpw4EDByhXrhzJycmW9I2JIL46chcBqOrFeb4uCUJsJZLU5Wx6XFD/xPNoEXpcENmjen777Teuv/56brrpJlSVP//80+2QjDEu8NXVE+Udt39S/0iod/WAJ/kv/N92erU7jaFXnuV2OK5RVV588UUGDx7M0aNHmTRpEgMHDqRMmbBdjsEYUwhf//Nb4unayZv4FQj5Vr/524IFC2jVqhVz5syhSZMmbodjjHGRr8T/VWno1jEny8zMZMaMGXTt2pV69eqRnJzMKaecYsM0jTE++/h/LO4bi8gYEflYRNaISPMc288RkfdEZJWILBSRssU9h8nfxo0bSUxM5MEHH2TevHkAxMXFWdI3xgA+En9xJ2qJSDuglqq2B/oBk3K+LXCtqrYDtgHXFecc5mTHjh1jzJgxtG7dmh9//JHXXnuNxx57zO2wjDEhxqkm4BXAAgBV/Qaolr1DVTeo6lHv07146vznIiJ9ReRzEfnclvPzX1JSEk888QTdunXj22+/5dZbb0Vs3oIxJg+nEn9NIGfGPi4iuc4lIolAczw3j3NR1VmqmqCqCfHx8Q6FGB6OHDnCtm3bAHjooYdYtmwZCxYswH5uxpiCOJX49wM5V+rI8i7mgngMwzMq6A5VzXQohrD38ccf07JlS7p27UpWVhZVq1blmmtKVWUNY4wLnEr8q4AbAESkGbA9x77+wA5VHWNJv3j2799P//796dChA1lZWTz99NN249YY4zenZvAsBzqJyCrgINBPRCYCI4BrgTgRyV63921VnexQHGHnu+++47LLLmPHjh0MHjyY0aNHU6FCBbfDMsaUIo4kfm+3zj15Ng/1fu/kxDnDXXZRtUaNGpGYmMjgwYNp06aN22EZY0oh6x8IcarKG2+8QUJCAgcOHKBs2bK8+eablvSNMcVmiT+EpaWlcd1119G9e3fKlCnDnj173A7JGBMGLPGHIFVl9uzZNGvWjPfff5/Jkyezdu1aGjZs6HZoxpgwYOUZQ9TChQs577zzmD17No0bN3Y7HGNMGLHEHyIyMzOZOnUqN9xwQ66iajbz1hgTaNbVEwK++eYb2rZty6BBg3jllVcAqFKliiV9Y4wjLPG76OjRo4wcOZJzzz2Xn3/+mQULFjB8+HC3wzLGhDlL/C5KSkpi1KhR3HjjjXz77bfccsst1so3xjjO+viD7PDhw+zatYuGDRsyaNAg2rZtS6dONqfNGBM81uIPog8++IBzzjmH66+//kRRNUv6xphgC+vEn5KaxrHMLGZ+9BOJEz4gJTXNlTj27dtHnz59uPTSS4mKiuLZZ5+1omrGGNeEbVdPSmoaQ5LXn3ieti/9xPMuresGLY5vv/2Wyy67jJ07d/LII48wcuRIYmNjg3Z+Y4zJK2ybnaOWbSQjU3Nty8hURi3bGJTzq3rO3bhxYy666CI+/fRTJk6caEnfGOO6sE38e49kFGl7oKgqr732Gueee+6JomoLFiwgISHB0fMaY4y/wjbxu+HXX3/l2muvpUePHpQvX569e/e6HZIxxpwkbBN/XGxMkbaXhKry/PPP07x5cz788EOmTJnC6tWradCgQcDPZYwxJRW2iX9k5+bEROWeDBUTJYzs3NyR8y1ZsoQ2bdqwYcMGHnzwQaKjox05jzHGlFTYjurJHrkz8M2vAKgbF8uQjmcGbETP8ePHmTJlCjfddBP169cnOTmZypUr28xbY0zIC9sWP3iSf9noKO7p0Jg1wy4JWNJfv349559/PkOGDOG1114DsEqaxphSI2wTf0pqGq1GvXdiAlfr0e+VeALX0aNHGTFiBAkJCWzfvp1FixYxbNiwAEVsjDHBEZaJPyU1jSGL1rMv/e+hm3uPZDAkeX2Jkn9SUhJJSUnceuutbNq0iRtuuMFa+caYUicsE/+kdzeTkaUnbc/IVCa9u7lI73X48GF+/vlnAAYPHsyKFSuYN28e1atXD0isxhgTbGGZ+H/bl16sfXm9//77tGjR4kRRtbi4ODp27BiIEI0xxjVhmfjrxBVcFqGwfdn27t1Lr169uPzyyylbtixTp061omrGmLARltlsSMczTxrDDxATLQzpeGahr920aRPNmjVj3rx5DBs2jPXr19OuXTunQjXGmKALy3H82cM2hyav56i3UFvVCjE8eW3zAod0ZmVlERUVRZMmTbjkkksYPHgw5557btBiNsaYYAnLxA+e5L/594PMXfUz34+9qsDjVJVXXnmFp59+mlWrVlGlSpUTY/ONMSYchWVXj7+2bdvGVVddxZ133knlypXZv3+/2yEZY4zjIjLxZ2VlMWPGDFq0aMHq1auZNm0aq1aton79+m6HZowxjgvbrp7CiAhvv/02iYmJvPDCC1ZF0xgTUSR7pahQlZCQoJ9//nmRX3fb7HWs+WnPiedtG5xC64PruPnmm2nQoAEHDx6kUqVKNvPWGBOWROQLVc13BSjHWvwiMga4yHuOvqq60bu9EjAbqAvsAe5Q1QOBPHfepH/s959Y8vJU3vj9J7Kyshg2bBiVK1cO5CmNMabUcKSPX0TaAbVUtT3QD5iUY/dDwDJVvQj4P+CeQJ8/O+nr8WPsXTmfHfMe4vihP6nRZbgVVTPGRDynWvxXAAsAVPUbEamWY98lwATv48XA8w7FwL61b3Jg3UIqtriMqpf0IjrWWvnGGONU4q8J7Mrx/LiIRKlqFlBOVbPLZv4JVM37YhHpC/QFSjTSpkqbrpSv14LY01oX+z2MMSbcODWccz+5E3qWN+kDZIlI9nmrkvsPBACqOktVE1Q1IT4+vsgnj/G+e1T5SrmSfkxEDl41xpjcnEqFq4AbAESkGbA9x75Pgeu8j7sB7wf65JNubFWk7cYYE0mcSvzLgbIisgp4GhgqIhNFpCwwHugrIh8B5wEvBfrkXVrXZcrNragbF4vgWW93ys2tArb0ojHGlGZhO47fGGMiWWHj+K3X2xhjIowlfmOMiTCW+I0xJsJY4jfGmAhjid8YYyJMyI/qEZFdwLYSvEUNYHeAwiktIu2aI+16wa45UpTkmhuoar4zYEM+8ZeUiHxe0JCmcBVp1xxp1wt2zZHCqWu2rh5jjIkwlviNMSbCRELin+V2AC6ItGuOtOsFu+ZI4cg1h30fvzHGmNwiocVvjDEmB0v8xhgTYcIm8YvIGBH5WETWiEjzHNsricgCEVkpIikicoqbcQZSIdd8joi8JyKrRGShtxx2WCjomnPsryUiR0SkvBvxOaGwaxaRu0XkE+++S92KMdAK+bddVkReEpEPROQdEaniZpyBJCLxIjJWRMbk2R7wHBYWid/txd3d4OOaFbhWVdvhmfx2XT5vUer4uOZswwijST6FXbM3IbYD/qmqiar6X5fCDCgfv+crgTRVvQRYAvR2IUSnPAMcBWLybA94DguLxE+exd2BvIu7L/I+Xgy0DW5ojinwmlV1g6oe9T7dCxwOfniOKOz3jIici+eP3pbgh+aYwq65F54/7B94P9nVcCE+JxR2zQf5e1nXGuSzdGtppap3ACvz2RXwHBYuiT/fxd29j30u7l5KFXbNAIhIItAceDeYgTmowGsWkQrABGCUG4E5qLDf8+nAblXtgCcxPBnk2JxS2DWvBpqKyCbgNmBpsINzQcBzWLgk/hIt7l5KFXjN4jEMT0vhDlXNdCNABxT2e34WmKiq+4MflqMKu+bjwDvex/8GmgUzMAcVds3jgKdVtRlwO5Extj/gOSxcEr+ri7u7pLBr7g/sUNUxYZT0oYBrFpGaeNZv7iMib+BJgC+7FGOgFfZ7Xgd08j7uAHwd1MicU9g1NwB2eh//AdQLbmiuCHgOC4sJXN6/hjOAFnj6APsB9wEjgFOAV4BY4EdgQI7+71LLxzWnAHHAMe/hb6vqZDfiDKTCrllVj+U47iPgSlX9y404A8nH77ks8BIQj6eV3FNV/3Qp1IDxcc2nAf/C02iNAYao6jqXQg04EemA59/uMBGZiEM5LCwSvzHGGP+FS1ePMcYYP1niN8aYCGOJ3xhjIowlfmOMiTCW+I0xJsJY4jcBJSIHROQj79eQAo7pICITSnCOu0Tke28Rr5Uick4RX5+UXcTNO3zupO3FjGukiHydo7jYuT6O71DYfu8xQ0UkUUQqisj7IpLvLGwRaeidw4CIvCwiZxXrIgqIUUSqi8grIhIdiPc17rLEbwJtk6p28H7lV0QtUCZ7i3j1BIo0R0FVH88xxn9CAduL6xFvXL3xjMEuTKF//Ly1dxqq6hrgXOBbVe1YwviKagKAd37Ah3jKJJhSzhK/cZSIJIjI/4nIahF5MZ/9M72t43UiEuMtQfu6t+zuv0WkWn7vm01Vf8QzsQURGSGeUtRrRGSKd1sTEfnQuz3Ju+0jESkvIouAZt7n1XJs/yzHJ4KuIvKEiNQWkbe8cb0pvktdN+bvmcUn/QzyOXfnHLHf7X2PLsAiEakNTAOuE5FnROQ0EfmP95PFv0UkbzXHAolIb28cK0XkVREpl/PTgveYN7zbpuWIsRnwBnCLv+cyocsSvwm07ETxkYh0Bn4GOuIpH9xAROpmHygiVYFmqpqIp7RwBp6yygu9ZXf/BQwo7GQicjWwXkQuBxoCF3nfL0ZErgWuBl71lqh+IudrVfVG/v6EsifHrsVAZ+/j24A5eEoDj/TG9TFwcwEhPSUiW/AkyEe82076GeQ8N5AFDMRTW+lCoIf3D08L4EtV3end/7qqDsZTqOs67yeLP4A2hf2McvyszgSuBzp4S/yuB/oWdLyq3p/j57NJVY8AFfw5lwltZdwOwISd7GQGgIh0Aq4CDuEpr1s5e5+q7vW2YKfjqTvzGp4ujfYiMhDPv8//FXCeQSJyM/ANngR7D7Bc/56K/j5wFp6p/4NEZDIwG/jWj2uYB/xLPKUfjqnqb977CM+KCEB5/i6Tm9cjwGd4iqdVA9KA8wv6GXidgafS5v95n9cAauFJskfyOcdZwJ0ichBPCYO87weAiLQBnvI+nYnnD8z7qnrcu+19PF1SbxdwLSZMWeI3TnsSSPQ+ztU/7e2ieEdV3xbPCkNfA98Di1V1lfeY2ALed7KqPp/jvTbiKWS1xLvpEuAtQFU1STyrFr3LybXMT+omUdWdIpIF3I+ntQ/wA/Cwqm4VTy2ZArtXVHWPiDyKZ2GNWwr5GWS/x894Cqxdo6oqIhVU9YiI7ATqAFvznGIE0FdVd4hIgUlbVT/DU7wNABFpAdwlIs95i/ddAqQCe4DaOV7aJMdjyxFhyLp6jNOWAl/iqZaZlmdfdWCNiHwARONJruOA4d5++WV4+sp9UtV3gN3eewUfAb+r6vvArSKyDk+rdl4+L93i7VvPW+N8PnC1qn7gff4o8KI31sXkWQQmn3g+AKLFsxxiQT+DLSKyCk955RRgnYi8Bwz37v8Iz6IkeS0C/isiyXiKs/nFu6jJf/j7Z94QeElVDwJrRWSGiDzJ38X9AFZ673mcKSKn4/nDbEo5K9JmTIgST7/Sa8BdOauPuhjPv4ApqmrJv5SzxG9MCPPekI1V1a9cjiMOuEBVV7gZhwkMS/zGGBNhrI/fGGMijCV+Y4yJMJb4jTEmwljiN8aYCGOJ3xhjIsz/B80A3q+y/IciAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ROC 커브\n",
    "fpr, tpr, thresholds = roc_curve(df1[\"Survived\"], model2.decision_function(df1[train_cols])) \n",
    "\n",
    "plt.plot(fpr, tpr, \"o-\", label=\"Logistic Regression\")\n",
    "\n",
    "# 기준선 추가\n",
    "plt.plot([0, 1], [0, 1], \"k--\", label=\"Random Guess\")\n",
    "\n",
    "plt.xlabel(\"False Positive Rate(fall-out)\")\n",
    "plt.ylabel(\"True Positive Rate(Recall)\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8414235345497928"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_auc_score(df1[\"Survived\"], model2.decision_function(df1[train_cols]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}