ch03_평가.ipynb

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      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/wlsisl/ESAA_22-2/blob/main/sklearn_evaluation.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "R0ZMfIBtQkfl"
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      "source": []
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    {
      "cell_type": "markdown",
      "source": [
        "## 평가 (evaluation)\n",
        "\n",
        "머신러닝 프로세스 구성 : 데이터 가공/변환, 모델 학습/예측, 평가\n",
        "\n",
        "여러가지 방법으로 예측 성능을 평가할 수 있는데 성능 평가 지표(evaluation Metric)는 일반적으로 모델이 분류냐 회귀냐에 따라 여러 종류로 나뉜다. \n",
        "\n",
        "회귀의 경우 대부분 실제 값과 예측값의 오차 평균값에 기반한다. \n",
        "ex) 오차에 절댓값을 씌운 뒤 평균 오차를 구하거나 오차의 제곱 값에 루트를 씌운 뒤 평균 오차를 구하는 방법\n",
        "\n",
        "#### [분류에 사용되는 평가 지표 방법]\n",
        " 0 과 1로 결정값이 한정되는 이진 분류의 성능 평가 지표에 대해서 집중적으로 알아보자. \n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "####분류의 성능 평가 지표\n",
        "\n",
        "- 정확도(Accuracy)\n",
        "- 오차행렬(confusion matrix)\n",
        "- 정밀도(precision)\n",
        "- 재현율(recall)\n",
        "- F1스코어\n",
        "- ROC AUC\n",
        "\n",
        "분류는 결정 클래스 값 종류의 유형에 따라 긍정/부정과 같은 2개의 결괏값만을 가지는 `이진 분류`와 여러 개의 결정 클래스 값을 가지는` 멀티 분류`로 나뉠 수 있다. \n",
        "\n",
        "분류의 성능 지표는 특히 이진 분류에서 더욱 중요하게 강조하는 지표이다.\n",
        "\n",
        "---\n",
        "### 01 정확도\n",
        "\n",
        "- 실제 데이터에서 예측 데이터가 얼마나 같은지를 판단하는 지표\n",
        "\n",
        "`정확도 = 예측 결과가 동일한 데이터 건수 / 전체 예측 데이터 건수`\n",
        "\n",
        "직관적으로 모델 예측 성능을 나타내는 평가지표이다.\n",
        "하지만 이진 분류의 경우 데이터의 구성에 따라 ml모델의 성능을 왜곡할 수 있기 때문에 정확도 수치 하나만 가지고 성능을 평가하지 않는다. 정확도 지표가 어떻게 ml 모델의 성능을 왜곡하는지 예제로 살펴보자.\n"
      ],
      "metadata": {
        "id": "azxOtU-wQlyy"
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    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.base import BaseEstimator\n",
        "\n",
        "class MyDummyClassifier(BaseEstimator):\n",
        "  #fit()메서드는 아무것도 학습하지 않음.\n",
        "  def fit(self, X, y=None):\n",
        "    pass\n",
        "  #predict()메서드는 단순히 Sex피처가 1이면 0, 그렇지 않으면 1로 예측함.\n",
        "  def predict(self, X):\n",
        "    pred = np.zeros((X.shape[0],1))\n",
        "    for i in range (X.shape[0]):\n",
        "      if X['Sex'].iloc[i] == 1:\n",
        "        pred[i] =0\n",
        "      else :\n",
        "        pred[i] =1\n",
        "    return pred"
      ],
      "metadata": {
        "id": "h7YaPsGuQlNE"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "이제 생성된  MyDummyClassifier를 이용해 앞장의 타이타닉 생존자 예측을 수행해보자. "
      ],
      "metadata": {
        "id": "rw9RHKoZVVtv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "titanic_df =pd.read_csv('/content/drive/MyDrive/titanic_train.csv')\n",
        "\n",
        "#이전 함수들 불러오기\n",
        "def fillna(df):\n",
        "  df['Age'].fillna(df['Age'].mean(), inplace=True)\n",
        "  df['Cabin'].fillna('N', inplace=True)\n",
        "  df['Embarked'].fillna('N', inplace=True)\n",
        "  df['Fare'].fillna(0, inplace=True)\n",
        "  return df\n",
        "                  \n",
        "def drop_features(df):\n",
        "  df.drop(['PassengerId', 'Name', 'Ticket'], axis=1, inplace=True)\n",
        "  return df\n",
        "\n",
        "def format_features(df):\n",
        "  df['Cabin']=df['Cabin'].str[:1]\n",
        "  features=['Cabin', 'Sex', 'Embarked']\n",
        "  for feature in features:\n",
        "    le=LabelEncoder()\n",
        "    le.fit(df[feature])\n",
        "    df[feature]=le.transform(df[feature])\n",
        "  return df\n",
        "\n",
        "def transform_features(df):\n",
        "  df=fillna(df)\n",
        "  df=drop_features(df)\n",
        "  df=format_features(df)\n",
        "  return df\n",
        "  \n",
        "#원본 데이터를 재로딩, 데이터 가공, 학습 데이터/테스트 데이터 분할.\n",
        "y_titanic_df = titanic_df['Survived']\n",
        "X_titanic_df = titanic_df.drop('Survived', axis=1)\n",
        "X_titanic_df = transform_features(X_titanic_df)\n",
        "X_train, X_test, y_train, y_test = train_test_split(X_titanic_df, y_titanic_df, test_size=0.2, random_state=0)\n",
        "\n",
        "#위에서 생성한 Dummy Classifier를 이용해 학습/예측/평가 수행\n",
        "\n",
        "myclf = MyDummyClassifier()\n",
        "myclf.fit(X_train, y_train)\n",
        "\n",
        "mypredictions = myclf.predict(X_test)\n",
        "print('Dummy Classifier의 정확도는 :{0:.4f}'.format(accuracy_score(y_test, mypredictions)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9eufn11uVWKf",
        "outputId": "2d715fcf-4950-401e-e0be-2dcd8cf2b854"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n",
            "Dummy Classifier의 정확도는 :0.7877\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "이렇게 단순한 알고리즘으로 예측을 하더라도 데이터의 구성에 따라 정확도는 꽤 높은 수치가 나올 수 있기 때문에 정확도를 평가 지표로 사용할 때는 신중해야한다. \n",
        "\n",
        "-> 특히 정확도는 불균형한 레이블 값 분포에서 ML모델의 성능을 판단할 경우, 적합한 평가 지표가 아니다. \n",
        "\n",
        "**[MNIST데이터 세트를 변환해 불균형한 데이터 세트로 만들고 정확도 지표 적용을 해보자]**\n",
        "- MNIST 데이터 세트는 0부터 9까지의 숫자 이미지의 픽셀 정보를 가지고 있고 이를 기반으로 숫자 Digit를 예측하는데 사용됨\n",
        "- 사이킷런은 load_digits() 를 통해 데이터 세트를 제공함."
      ],
      "metadata": {
        "id": "98kV53kVVWUM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#전체데이터의 10%만 true, 나머지는 false로 변형하기 \n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.base import BaseEstimator\n",
        "from sklearn.datasets import load_digits\n",
        "\n",
        "class MyFakeClassifier(BaseEstimator):\n",
        "  def fit(self, X, y):\n",
        "    pass\n",
        "  \n",
        "  #입력값으로 들어오는 X데이터 세트의 크기만큼 모두 0값으로 만들어서 반환\n",
        "  def predict(self, X):\n",
        "    return np.zeros( (len(X),1),dtype=bool)\n",
        "\n",
        "#사이킷런의 내장 데이터 세트인 load_digits()를 이용해 MNIST데이터 로딩\n",
        "digits= load_digits()\n",
        "\n",
        "#digits번호가 7번이면 true이고 이를 astype(int)로 1로 변환, 그게 아니면 0으로 변환\n",
        "y=(digits.target==7).astype(int)\n",
        "X_train, X_test, y_train, y_test = train_test_split(digits.data, y, random_state=11)\n",
        "\n"
      ],
      "metadata": {
        "id": "lD7uFgdZVWad"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "불균형한 데이터로 생성한 y_test의 데이터 분포도를 확인하고 MyFakeClassifier을 이용해 예측고 ㅏ평가를 수행해보자.\n"
      ],
      "metadata": {
        "id": "mE04mIELVWgW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#불균형한 레이블 데이터 분포도 확인\n",
        "print('레이블 테스트 세트 크기:',y_test.shape)\n",
        "print('테스트 세트 레이블 0과 1의 분포도')\n",
        "print(pd.Series(y_test).value_counts())\n",
        "\n",
        "#Dummy Classifier로 학습/예측/정확도 평가\n",
        "fakeclf=MyFakeClassifier()\n",
        "fakeclf.fit(X_train, y_train)\n",
        "fakepred=fakeclf.predict(X_test)\n",
        "print('모든 예측을 0으로 하여도 정확도는:{:.3f}'.format(accuracy_score(y_test, fakepred)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C5BBpFBRcC0Q",
        "outputId": "3d464a89-daaa-4334-b2f3-c2d65f41560b"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "레이블 테스트 세트 크기: (450,)\n",
            "테스트 세트 레이블 0과 1의 분포도\n",
            "0    405\n",
            "1     45\n",
            "dtype: int64\n",
            "모든 예측을 0으로 하여도 정확도는:0.900\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "단순히 predict()결과를 np.zeros()로 모두 0값으로 반환함에도 불구하고 450개의 테스트 데이터 세트에 수행한 예측 정확도는 90%이다. 단지 모든 것을 0으로만 예측해도 이런 결과가 나눈다. \n",
        "\n",
        "따라서 불균형한 데이터 세트에서는 성능 수치로 사용해서는 안 됨.\n",
        "\n",
        "이를 극복하기 위해 여러가지 분류 지표와 함께 적용해야함.\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "## 02 오차행렬\n",
        "* 이진 분류에서 성능 짚로 잘 활용되는 오차행렬은 학습된 분류 모델이 예측을 수행하면서 얼마나 헷갈리고 있는지도 함께 보여주는 지표이다.\n",
        "\n",
        "4분면 행렬에서 실제 레이블 클래스 값과 예측 레이블 클래스 값이 어떠한 유형을 가지고 매핑되는지를 나타낸다. 4분면의 왼쪽, 오른쪽을 예측된 크랠스 값 기준으로 Negative와 positive로 분류하고, 4분면의 위, 아래를 실제 클래스 값 기준으로 Negative와 Positive로 분류하면 예측 클래스와 실제 클래스의 값 유형에 따라 결정되는 TN, FP, FN, TP형태로 오차 행렬의 4분면을 채울 수 있다. \n",
        "\n",
        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      ],
      "metadata": {
        "id": "wIBtJyK6cC64"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "TN은 True Negative의 의미이고 \n",
        "\n",
        "앞의 True는 예측 클래스 값과 실제 클래스 값이 같다\n",
        "\n",
        "Begative는 예측값이 Negative값이라는 의미이다. \n",
        "\n",
        "즉 TN = 예측을 negative로 했는데 실제값도 negative값 0 이라는 의미임.\n",
        "\n",
        "* FP : 예측값을 P 값 1로, 실제값은 N값 0\n",
        "* FN : 예측값을 N값 0로, 실제값은 P 값 1\n",
        "* TP : 예측값을 P 값 1로, 실제값은 P 값 1\n",
        "* FN : 예측값을 N값 0로, 실제값은 N값 0\n",
        "\n",
        "\n",
        "---\n",
        "사이킷런은 오차행렬을 구하기 위해 confusion_matrix() 를 제공함.\n"
      ],
      "metadata": {
        "id": "YmdskF7Ugnka"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "\n",
        "\n",
        "confusion_matrix(y_test, fakepred)\n",
        "\n",
        "#출력된 오차행렬은 ndarray형태이다. "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "54cOEQEQcDA9",
        "outputId": "403002ca-1ae5-4a14-f6ae-00da22c2254e"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[405,   0],\n",
              "       [ 45,   0]])"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "해석 : TN은 array[0,0]로 405인 것이다. 전체 405건 데이터 중 무조건 Negative 0으로 예측해서 true가 된 결과 405건.\n",
        "\n",
        "\n",
        "앞 절의 MyFakeClassifier는 load_digits()에서 target==7인지 아닌지에 따라 클래스 값을 True/False 이진 분류로 변경한 데이터 세트를 사용해 무조건 Negative로 예측하는 Classifier였고 테스트 데이터 세트의 클래스 값 분포는 0이 405건, 1이 45건이다. \n",
        "\n",
        "\n",
        "-> 이 값들을 조합하여 classifier의 성능을 측정할 수 있는 주요 지표인 정확도, 정밀도, 재현율 값을 알 수 있다. \n",
        "\n",
        "정확도 = (TN + TP) / (TN + FP + FN + TP)\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "## 03 정밀도와 재현율\n",
        "\n",
        "positive 데이터 세트의 예측 성능에 좀 더 초점을 맞춘 평가 지표이다. \n",
        "앞서 만든 MyFakeClassifier는 Positive로 예측한 tp값이 하나도 없기 때문에 정밀도와 재현율 값이 모두 0이다.\n",
        "\n",
        "* **정밀도= TP/ (FP + TP)**\n",
        "\n",
        "-정밀도는 예측을 Positive로 한 대상 중에 예측과 실제 값이 일치한 데이터의 비율을 뜻한다. \n",
        "\n",
        "\n",
        "* **재현율= TP/ (FN + TP)**\n",
        "\n",
        "-재현율은 실제 값이 Positive인 대상 중에 에측과 실제 값이 Positive로 일치한 데이터의 비율을 뜻한다. 민감도 또는 TPR이라고도 불림.\n",
        "\n",
        "[어떤 것이 더 우선인지]\n",
        "\n",
        "* 재현율이 중요 지표인 경우 : 실제 Positive양성 데이터를 Negative로 잘못 판단하게 되면 업무상 큰 영향이 발생하는 경우.\n",
        "\n",
        "* 정밀도가 중유한 경우 : 실제 Negative음성인 데이터 예측을 Positive양성으로 잘못 판단하게 되어 업무상 영향이 발생하는 경우\n",
        "\n",
        "-> 재현율과 정밀도 모두 높은 수치를 얻는 것이 좋은 성능평가이다. \n"
      ],
      "metadata": {
        "id": "zX34LxgFcDFq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score, precision_score, recall_score, confusion_matrix\n",
        "\n",
        "def get_clf_eval(y_test, pred):\n",
        "  confusion = confusion_matrix(y_test, pred)\n",
        "  accuracy = accuracy_score(y_test, pred)\n",
        "  precision = precision_score(y_test, pred)\n",
        "  recall = recall_score(y_test, pred)\n",
        "  print('오차행렬')\n",
        "  print(confusion)\n",
        "  print('정확도:{0:.4}, 정밀도:{1:.4f}, 재현율:{2:.4f}'.format(accuracy, precision, recall))\n"
      ],
      "metadata": {
        "id": "rmDnO6awcDK3"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "\n",
        "#원본 데이터를 재로딩, 데이터 가공, 학습 데이터/테스트 데이터 분할.\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "titanic_df =pd.read_csv('/content/drive/MyDrive/titanic_train.csv')\n",
        "\n",
        "y_titanic_df = titanic_df['Survived']\n",
        "X_titanic_df = titanic_df.drop('Survived', axis=1)\n",
        "X_titanic_df = transform_features(X_titanic_df)\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X_titanic_df, y_titanic_df, test_size=0.20, random_state=11)\n",
        "\n",
        "lr_clf = LogisticRegression()\n",
        "\n",
        "lr_clf.fit(X_train, y_train)\n",
        "pred = lr_clf.predict(X_test)\n",
        "get_clf_eval(y_test, pred)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V6CqXqfexeYK",
        "outputId": "600b3665-40ce-48d3-e315-d57111553091"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n",
            "오차행렬\n",
            "[[104  14]\n",
            " [ 13  48]]\n",
            "정확도:0.8492, 정밀도:0.7742, 재현율:0.7869\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:818: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG,\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "결과: 정밀도에 비해 재현율이 낮게 나왔다. 재현율 또는 정밀도를 좀 더 강화할 방법은?\n",
        "\n",
        "### 정밀도/재현율 프레이드오프\n",
        "\n",
        "분류하려는 업무의 특성상 정밀도 또는 재현율이 특별히 강조돼야 할 경우 분류의 결정 임걔값을 조정해 정밀도 또는 재현율의 수치를 높일 수 있다.\n",
        "\n",
        "하짐나 정밀도와 재현율은 상호 보완적인 평가지표이기 때문에 어느 한쪽을 강제로 높이면 다른 하나의 수치는 떨어지기 쉽다.\n",
        "\n",
        "이를 정밀도/재현율의 프레이드오프라고 부른다.\n",
        "\n",
        "------\n",
        "사이킷런의 분류 알고리즘은 예측 데이터가 특정 레이블에 속하는지를 계산하기 위해 먼저 개별 레이블별로 결정 확률을 구한다. 그리고 예측 확률이 큰 레이블값으로 예측하게 된다.\n",
        "\n",
        "`predict_proba() : 개별 데이터별로 예측 확률을 반환하느 메서드`\n",
        "\n",
        "* 학습이 완료된 사이킷런 Classifier 객체에서 호출이 가능하고 테스트 피처 데이터 세트를 파라미터로 입력해주면 테스트 피처 레코드의 개별 클래스 예측 확률을 반환한다. 반환 결과가 예측 겨로가 클래스 값이 아닌 예측 확률 결과이다.\n",
        "\n",
        "\n",
        "* 입력 파라미터 : predict() 메서드와 동일하게 보통 테스트 피처 데이터 세트를 입력\n",
        "\n",
        "* 반환 값: 개별 클래스의 예측 확률을 ndarray m x n (m: 입력 값의 레코드 수, n: 클래스 값 유형) 형태로 반환한다.\n",
        "\n",
        "각 열은 개별 클래스의 예측 확률이고 이진 분류에서 첫번째 칼럼은 - Negative의 확률, 두번째 칼럼은 1 positive의 확률이다."
      ],
      "metadata": {
        "id": "K264Yg10cDPH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pred_proba = lr_clf.predict_proba(X_test)\n",
        "pred - lr_clf.predict(X_test)\n",
        "print('pred_proba()결과 Shape : {0}'.format(pred_proba.shape))\n",
        "print('pred_proba array 에서 앞 3개만 샘프로 추출 \\n:',pred_proba[:3])\n",
        "\n",
        "#예측 확률 array와 예측 결괏값 array를 병합해 예측 확률과 결괏값을 한눈에 확인\n",
        "pred_proba_result = np.concatenate([pred_proba, pred.reshape(-1,1)], axis=1)\n",
        "print('두 개의 class 중에서 더 큰 확률을 클래스 값으로 예측 \\n', pred_proba_result[:3])"
      ],
      "metadata": {
        "id": "Tyq2B111cDUR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8cb699bb-723e-41b3-8977-a58809660b2a"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "pred_proba()결과 Shape : (179, 2)\n",
            "pred_proba array 에서 앞 3개만 샘프로 추출 \n",
            ": [[0.46191519 0.53808481]\n",
            " [0.878675   0.121325  ]\n",
            " [0.87716185 0.12283815]]\n",
            "두 개의 class 중에서 더 큰 확률을 클래스 값으로 예측 \n",
            " [[0.46191519 0.53808481 1.        ]\n",
            " [0.878675   0.121325   0.        ]\n",
            " [0.87716185 0.12283815 0.        ]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "결과: 0과 1에 대한 확률을 나타내므로 첫번째 칼럼값과 두번째 칼럼값을 더하면 1이 된다. \n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "predict() 는 pred_proba 메서드가 반환하는 확률 값을 가진 ndarray에서 정해진 임곗값( ex 0.5) 을 만족하는 ndarray의 칼럼 위치를 최정 얘측 클래스로 결정한다.\n",
        "\n",
        "이러한 구현을 위해 사이킷런의 Binarizer클래스를 이용한다.\n",
        "먼저 <Binarizer클래스 사용법> 부터 알아보자.\n",
        "\n",
        "### **<Binarizer클래스 사용법>**"
      ],
      "metadata": {
        "id": "3C3EM_3GcDXz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import Binarizer\n",
        "\n",
        "X=[[1,-2,2], [2,0,0],[0,1.1,1.2]]\n",
        "\n",
        "#X의 개별 원소들이 threshold값보다 같거나 작으면 0을, 크면 1을 반환\n",
        "binarizer = Binarizer(threshold = 1.1)\n",
        "print(binarizer.fit_transform(X))"
      ],
      "metadata": {
        "id": "cUxlYeeNcDbk",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c74a2609-8e7b-4bd9-fb6e-0b938ffcea47"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[0. 0. 1.]\n",
            " [1. 0. 0.]\n",
            " [0. 0. 1.]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "입력된 X데이터 세트에서 Binarizer의 threshold값이 1.1보다 같거나 작으면 0, 크면 1로 변환됨을 알 수 있다.\n",
        "\n",
        "이제 이 것을 이용해 사이킷런 predict()의 의사코드를 만들어 보자. \n",
        "\n",
        "바로 앞 예제의 LogisticRegression 객체의 predict_proba() 메서드로 구한 각 클래스별 예측 확률값인 pred_proba 객체 변수에 분류 결정 임계값을 0.5로 지정한  Binarizer 클래스를 적용해 최종 예측값을 구하는 방식이다.\n",
        "\n"
      ],
      "metadata": {
        "id": "Mjbu0mhfcDf4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import Binarizer\n",
        "\n",
        "# Binarizer의 threshold설정값. 분류 결정 임곗값임.\n",
        "custom_threshold = 0.5\n",
        "\n",
        "#predict_proba() 반환값의 두번재 칼럼, 즉 positive클래스 칼럼 하나만 추출해  Binarizer를 적용\n",
        "pred_proba_1 = pred_proba[:,1].reshape(-1,1)\n",
        "\n",
        "binarizer= Binarizer(threshold=custom_threshold).fit(pred_proba_1)\n",
        "custom_predict = binarizer.transform(pred_proba_1)\n",
        "\n",
        "get_clf_eval(y_test, custom_predict)\n"
      ],
      "metadata": {
        "id": "zoXoyATAcDjo",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a813d0c1-b0b3-439d-9472-c6006bb3b643"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "오차행렬\n",
            "[[104  14]\n",
            " [ 13  48]]\n",
            "정확도:0.8492, 정밀도:0.7742, 재현율:0.7869\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "-> predict 가   predict_proba()를 기반하고 있다는 것을 알 수 있다. "
      ],
      "metadata": {
        "id": "cbAulzfMcDnQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Binarizer의 threshold설정값을 0.4로 설정. 즉 분류 결정 임곗값을 0.5에서 0.4로 낮춤\n",
        "custom_threshold = 0.4\n",
        "pred_proba_1 = pred_proba[:,1].reshape(-1,1)\n",
        "binarizer= Binarizer(threshold=custom_threshold).fit(pred_proba_1)\n",
        "custom_predict = binarizer.transform(pred_proba_1)\n",
        "\n",
        "get_clf_eval(y_test, custom_predict)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-HdBeiup7XDL",
        "outputId": "b1cf7cd3-5e81-4d61-e8e8-60b8fbf96b12"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "오차행렬\n",
            "[[98 20]\n",
            " [10 51]]\n",
            "정확도:0.8324, 정밀도:0.7183, 재현율:0.8361\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "임계값을 낮추니 재현율 값이 올라가고 정밀도가 떨어졌다. 이유는 분류 결정 임걧값은 positive예측값을 결정하는 확률의 기준이 된다. 확률이 0.5가 아닌 0.4부터 positive로 예측을 너그럽게 하기 때문에 임곗값 값을 낮출수록 True값이 많아지게 된다."
      ],
      "metadata": {
        "id": "M-meedC57XIx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 함수를 만들어서 임걧값을 변화하며 평가 지표를 조사해보자.\n",
        "\n",
        "#테스트를 수행할 모든 임걧값을 리스트 객체로 저장.\n",
        "thresholds = [0.4, 0.45, 0.5, 0.55, 0.60]\n",
        "\n",
        "def get_eval_by_threshold(y_test, pred_proba_c1, thresholds):\n",
        "  #threshold list객체 내의 값을 차례로 iteration하면서 evaluation수행\n",
        "  for custom_threshold in thresholds:\n",
        "    binarizer= Binarizer(threshold=custom_threshold).fit(pred_proba_1)\n",
        "    custom_predict = binarizer.transform(pred_proba_1)\n",
        "    print('임곗값:',custom_threshold)\n",
        "    get_clf_eval(y_test, custom_predict)\n",
        "\n",
        "get_eval_by_threshold(y_test, pred_proba[:,1].reshape(-1,1), thresholds)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IL-UfC087XNv",
        "outputId": "6ae3e36d-ffe6-4554-c5bd-656f38548851"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "임곗값: 0.4\n",
            "오차행렬\n",
            "[[98 20]\n",
            " [10 51]]\n",
            "정확도:0.8324, 정밀도:0.7183, 재현율:0.8361\n",
            "임곗값: 0.45\n",
            "오차행렬\n",
            "[[103  15]\n",
            " [ 12  49]]\n",
            "정확도:0.8492, 정밀도:0.7656, 재현율:0.8033\n",
            "임곗값: 0.5\n",
            "오차행렬\n",
            "[[104  14]\n",
            " [ 13  48]]\n",
            "정확도:0.8492, 정밀도:0.7742, 재현율:0.7869\n",
            "임곗값: 0.55\n",
            "오차행렬\n",
            "[[109   9]\n",
            " [ 15  46]]\n",
            "정확도:0.8659, 정밀도:0.8364, 재현율:0.7541\n",
            "임곗값: 0.6\n",
            "오차행렬\n",
            "[[112   6]\n",
            " [ 16  45]]\n",
            "정확도:0.8771, 정밀도:0.8824, 재현율:0.7377\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "- precision_recall_curve() API\n",
        "\n",
        "<입력 파라미터>\n",
        "\n",
        "y_true : 실제 클래스 값 배열 (배열 크기 = [데이터 건수])\n",
        "probas_pred : Positive 칼럼의 예측 확률 배열 (배열 크기 = [데이터 건수])\n",
        "\n",
        "<반환 값>\n",
        "\n",
        "정밀도: 임곗값별 정밀도 값을 배열로 반환\n",
        "재현율: 임곗값별 재현율 값을 배열로 반환"
      ],
      "metadata": {
        "id": "iD-fPgeS7XRw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import precision_recall_curve\n",
        "\n",
        "#레이블 값이 1일 때의 예측 확률을 추출\n",
        "pred_proba_class1 = lr_clf.predict_proba(X_test)[:,1]\n",
        "\n",
        "#실제값 데이터 세트와 레이블 값이 1일 때의 예측 확률을 precision_recall_curve인자로 입력\n",
        "precisions, recalls, thresholds = precision_recall_curve(y_test, pred_proba_class1)\n",
        "print('반환된 분류 결정 임곗값 배열의 Shape:', thresholds.shape)\n",
        "\n",
        "#반환된 임계값 배열 로우가 147건이므로 샘플로 10건만 추출하되, 임곗값을 15 step으로 추출\n",
        "thr_index = np.arange(0, thresholds.shape[0],15)\n",
        "print('샘플 추출을 위한 임계값 배열의 index 10개:', thr_index)\n",
        "print('샘플용 10개의 임곗값:', np.round(thresholds[thr_index],2))\n",
        " \n",
        "#15 step 단위로 추출된 임계값에 따른 정밀도와 재현율 값\n",
        "print('샘플 임계값별 정밀도:', np.round(precisions[thr_index],3))\n",
        "print('샘플 임계값별 재현율:', np.round(recalls[thr_index],3))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-hFyTLFh7XV8",
        "outputId": "135028f4-21cd-4611-fa47-d395f81fc22e"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "반환된 분류 결정 임곗값 배열의 Shape: (143,)\n",
            "샘플 추출을 위한 임계값 배열의 index 10개: [  0  15  30  45  60  75  90 105 120 135]\n",
            "샘플용 10개의 임곗값: [0.1  0.12 0.14 0.19 0.28 0.4  0.56 0.67 0.82 0.95]\n",
            "샘플 임계값별 정밀도: [0.389 0.44  0.466 0.539 0.647 0.729 0.836 0.949 0.958 1.   ]\n",
            "샘플 임계값별 재현율: [1.    0.967 0.902 0.902 0.902 0.836 0.754 0.607 0.377 0.148]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 곡선으로 시각화해보자.\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.ticker as ticker\n",
        "%matplotlib inline\n",
        "\n",
        "def precision_recall_curve_plot(y_test, pred_proba_c1):\n",
        "  #threshold ndarray와 이 threshold에 따른 정밀도, 재현율 ndarray추출.\n",
        "  precisions, recalls, thresholds = precision_recall_curve(y_test, pred_proba_c1)\n",
        "\n",
        "  #X축을 threshold값으로, y축은 정밀도, 재현율 값으로 각각 plot수행. 정밀도는 점선으로 표시\n",
        "  plt.figure(figsize=(8,6))\n",
        "  threshold_boundary = thresholds.shape[0]\n",
        "  plt.plot(thresholds, precisions[0:threshold_boundary],linestyle='--', label ='precision')\n",
        "  plt.plot(thresholds, recalls[0:threshold_boundary], label ='recall')\n",
        "\n",
        "  #threshold 값 X축의 Scale을 0.1 단위로 변경\n",
        "  start, end = plt.xlim()\n",
        "  plt.xticks(np.round(np.arange(start, end, 0.1), 2))\n",
        "\n",
        "  #x축, y축 label과 legend, 그리고 grid설정\n",
        "  plt.xlabel('Threshold value'); plt.ylabel('Precision and Recall value')\n",
        "  plt.legend(); plt.grid()\n",
        "  plt.show()\n",
        "\n",
        "precision_recall_curve_plot(y_test, lr_clf.predict_proba(X_test)[:,1])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 388
        },
        "id": "7nVHKbltJ95z",
        "outputId": "20670810-0cfb-41be-ce6c-983e7af535cf"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "결과: 임계값을 계속 증가시킬수록 재현율 값이 낮아지고 정밀도 값이 높아지는 반대의 양상이 된다. 앞 예제의 로지스틱 회귀 기반의 타이타닉 생존자 예측 모델의 경우 임곗값이 약 0.45 지점에서 재현율과 정밀도가 비슷해지는 모습을 보임\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "### 정밀도와 재현율이ㅡ 맹점\n",
        "positive예측의 임곗값을 변경함에 따라 정밀도와 재현율의 수치가 변경된다. 임곗값의 이러한 변경은 업무 환경에 맞게 두 개의 수치를 상호 보완할 수 있는 수준에서 적용돼야 한다. 그렇지 않고 단순히 하나의 성능 지표 수치를 높이기 위한 수단으로 사용돼서는 안 된다. \n",
        "\n",
        "- 정밀도 100%가 되는 방법\n",
        "\n",
        "확실하 닉준이 되는 경우만 Positive로 예측하고 나머지는 모두 Negative로 예측합니다. 예를 들어 환자가 80세 이상이고 비만이며 이전에 암 진단을 받았고 암 세포의 크기가 상위 0.1% 이상이면 무조건 Positive, 다른 경우는 Negative로 예측하는 거다.\n",
        "\n",
        "- 재현율 100%가 되는 방법\n",
        "\n",
        "모든 환자를 Positive로 얘측하면 된다. 재현율은 = TP/ (TP+FN)이므로 전체 환자 1000명을 다 pOSITIVE로 예측하는 거다. 이 중 실제 양성인 사람이 30명 정도라도 tn이 수치에 포함되지 않고 fn은 아예 0이므로 100%가 된다.\n",
        "\n",
        "\n",
        "이처럼 정밀도와 재현율 성능 수치도 어느 한족만 참조하면 극단적인 수치 조작이 가능하다. 따라서 정밀도 또는 재현율 중 하나만 스코어가 좋고 다른 하나는 스코어가 나쁜 분류는 성능이 좋지 않은 ㅜㅂㄴ류로 간주할 수 있다. 둘 중 하나만 강조하는 상항외 돼서는 안됨.\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "### 04 F1 스코어\n",
        "\n",
        "정밀도와 재현율을 결합한 지표 \n",
        "\n",
        "어느 한쪽으로 치우치지 않는 수치를 나타낼 때 상대적으로 높은 값을 가진다.\n",
        "\n",
        "사이킷런 제공 API : f1_score()\n"
      ],
      "metadata": {
        "id": "ucdwkepq7XZZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import f1_score\n",
        "f1= f1_score(y_test, pred)\n",
        "print('F1 스코어:{0:.4f}'.format(f1))"
      ],
      "metadata": {
        "id": "28a2FezWJ9c1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "def get_clf_eval(y_test, pred):\n",
        "  confusion = confusion_matrix(y_test, pred)\n",
        "  accuracy = accuracy_score(y_test, pred)\n",
        "  precision = precision_score(y_test, pred)\n",
        "  recall = recall_score(y_test, pred)\n",
        "  #F1 스코어 추가\n",
        "  f1 = f1_score(y_test , pred)\n",
        "  print('오차행렬')\n",
        "  print(confusion)\n",
        "  #f1 score print추가\n",
        "  print('정확도:{0:.4}, 정밀도:{1:.4f}, 재현율:{2:.4f}'.format(accuracy, precision, recall,f1))\n",
        "\n",
        "thresholds= [0.4, 0.45, 0.50, 0.55, 0.60]\n",
        "pred_proba = lr_clf.predict_proba(X_test)\n",
        "get_eval_by_threshold(y_test, pred_proba[:,1].reshape(-1,1), thresholds)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CotdrVHJoXdN",
        "outputId": "61503411-7bfc-4f52-9490-f3b605fa35d1"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "임곗값: 0.4\n",
            "오차행렬\n",
            "[[98 20]\n",
            " [10 51]]\n",
            "정확도:0.8324, 정밀도:0.7183, 재현율:0.8361\n",
            "임곗값: 0.45\n",
            "오차행렬\n",
            "[[103  15]\n",
            " [ 12  49]]\n",
            "정확도:0.8492, 정밀도:0.7656, 재현율:0.8033\n",
            "임곗값: 0.5\n",
            "오차행렬\n",
            "[[104  14]\n",
            " [ 13  48]]\n",
            "정확도:0.8492, 정밀도:0.7742, 재현율:0.7869\n",
            "임곗값: 0.55\n",
            "오차행렬\n",
            "[[109   9]\n",
            " [ 15  46]]\n",
            "정확도:0.8659, 정밀도:0.8364, 재현율:0.7541\n",
            "임곗값: 0.6\n",
            "오차행렬\n",
            "[[112   6]\n",
            " [ 16  45]]\n",
            "정확도:0.8771, 정밀도:0.8824, 재현율:0.7377\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "결과 : 0.6일때 가장 좋은 값을 보여준다. 하지만 임곗값이 0.6인 경우에는 재현율이 크게 감소하고 있으니 주지해야한다.\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "### 05 ROC곡선과 AUC\n",
        "\n",
        "* 이진 분류의 예측 성능 측정에서 중요하게 사용되는 지표\n",
        "* FPR(FALSE POSITIVE RATE)이 변할 때 TPR(재현율=민감도)이 어떻게 변하는지를 나타내는 곡선\n",
        "* 직선에 가까울수록 성능이 떨어지고 멀어질수록 성능이 뛰어나다.\n",
        "\n",
        "사이킷런 API : roc_curve()\n",
        "\n",
        "-입력 파라미터 \n",
        "\n",
        "* y_true : 실제 클래스 값 array (array shape= [데이터 건수]\n",
        "\n",
        "* y_score : predict_proba()의 반환 값 array에서 positive칼럼의 예측 확률이 보통 사용됨. array.shape = [n_samples]\n",
        "\n",
        "-반환 값 : array로 반환\n",
        "\n",
        "\n",
        "\n",
        "+ 민감도에 대응하는 지표 TNR(특이성)\n",
        "\n",
        "<정리>\n",
        "* 민감도(TPR)는 실제값 P양성이 정확히 예측 돼야 하는 수준을 나타낸다. ( 질병 ㅇ 테스트 ㅇ)\n",
        "\n",
        "* 특이성(TNR)은 실제값 N음성이 정확히 예측돼야하는 수준을 나타낸다.( 질병X 테스트도 음성)\n",
        "\n",
        "<식>\n",
        "\n",
        "TNR = TN / (FP + TN)\n",
        "\n",
        "FPR = FP/ (FP + TN) = 1- 특이성\n",
        "\n"
      ],
      "metadata": {
        "id": "LJzzm3BZoGFp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import roc_curve\n",
        "\n",
        "#레이블 값이 1일 때의 예측 확률을 추출\n",
        "pred_proba_class1 = lr_clf.predict_proba(X_test)[:,1]\n",
        "\n",
        "fprs, tprs, thresholds = roc_curve(y_test, pred_proba_class1)\n",
        "\n",
        "#반환된 임곗값 배열 로우가 47건이므로 샘플로 10건만 추출하되, 임곗값을 5 step으로 추출.\n",
        "#thredsholds[0]은 max(예측확률)+1로 임의 설정됨. 이를 제외하기 위해 np.arange는 1부터 시작\n",
        "thr_index = np.arange(1, thresholds.shape[0],5)\n",
        "print('샘플 추출을 위한 임계값 배열의 index 10개:', thr_index)\n",
        "print('샘플용 10개의 임곗값:', np.round(thresholds[thr_index],2))\n",
        " \n",
        "#5 step 단위로 추출된 임계값에 따른 정밀도와 재현율 값\n",
        "print('샘플 임계값별 FPR:', np.round(precisions[thr_index],3))\n",
        "print('샘플 임계값별 TPR:', np.round(recalls[thr_index],3))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2ZtU7mA6oGS0",
        "outputId": "5a89aefb-6e41-431e-be0b-64a6fa1e056e"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "샘플 추출을 위한 임계값 배열의 index 10개: [ 1  6 11 16 21 26 31 36 41 46 51]\n",
            "샘플용 10개의 임곗값: [0.97 0.65 0.63 0.56 0.45 0.4  0.35 0.15 0.13 0.11 0.11]\n",
            "샘플 임계값별 FPR: [0.385 0.393 0.428 0.436 0.449 0.451 0.47  0.491 0.519 0.545 0.573]\n",
            "샘플 임계값별 TPR: [0.984 0.967 0.967 0.951 0.934 0.902 0.902 0.902 0.902 0.902 0.902]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "결과 : 임곗값이 1에 가까운 값에서 점점 작아지면서 FPR이 점점 커진다. 이것이 커질 때 TPR은 가파르게 커짐을 알 수 있다. "
      ],
      "metadata": {
        "id": "htSv3va3oGdF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def roc_curve_plot(y_test, pred_proba_c1):\n",
        "  #임곗값에 따른 fpr, tpr 값을 반화받음.\n",
        "  fprs, tprs, thresholds = roc_curve(y_test, pred_proba_c1)\n",
        "  #ROC곡선을 그래프 곡선으로 그림.\n",
        "  plt.plot(fprs, tprs, label='ROC')\n",
        "  #가운데 대각선 직선을 그림.\n",
        "  plt.plot([0,1],[0,1],'k--', label='Random')\n",
        "\n",
        "  #FPR X축의 scale을 0.1 단위로 변경,  x, y 축 명 설정 등\n",
        "  start, end = plt.xlim()\n",
        "  plt.xticks(np.round(np.arange(start, end, 0.1), 2))\n",
        "  plt.xlim(0,1); plt.ylim(0,1)\n",
        "  plt.xlabel('FPR(1-Sensitivity)'); plt.ylabel('TPR(Recall)')\n",
        "  plt.legend()\n",
        "\n",
        "roc_curve_plot(y_test, pred_proba[:,1])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "WYV2jWyUoGnw",
        "outputId": "80409f67-5b4c-40c7-d515-bd6b4ba270dc"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "결과 : 일반적으로 ROC곡선 자체는 FPR과 TPR의 변화 값을 보고, 분류의 성능 지표로 사용되는 것은 ROC 곡선 면적에 기반한 AUC값으로 결정한다.\n",
        "\n",
        "**AUC 값 : ROC 곡선 밑의 면적을 구한 것**\n",
        "\n",
        "* 1에 가까울수록 좋은 수치이다.\n",
        "* AUC가 커지려면 FPR이 작은 상태에서 얼마나 큰 TPR을 얻을 수 있느냐가 관건이다."
      ],
      "metadata": {
        "id": "WVYh4vNHs077"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score, confusion_matrix, precision_score\n",
        "from sklearn.metrics import recall_score, f1_score, roc_auc_score\n",
        "import numpy as np\n",
        "\n",
        "print(confusion_matrix(y_test, pred))\n",
        "print(\"정확도:\", np.round(accuracy_score(y_test, pred),4))\n",
        "print(\"정밀도:\", np.round(precision_score(y_test, pred),4))\n",
        "print(\"재현율:\", np.round(recall_score(y_test, pred),4))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kghcLtScs1Af",
        "outputId": "db22e31e-1fb9-4cdb-bf35-90a21d8ad89d"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[104  14]\n",
            " [ 13  48]]\n",
            "정확도: 0.8492\n",
            "정밀도: 0.7742\n",
            "재현율: 0.7869\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "타이타닉 생존자 예측 로지스틱 회귀 모델의 roc auc 값은 약 0.8987로 측정됨.\n",
        "\n",
        "밑에 함수는 ROC, AUC 값까지 출력할 수 있다."
      ],
      "metadata": {
        "id": "bP5Rhj0Fs1E4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "def get_clf_eval(y_test, pred):\n",
        "  confusion=confusion_matrix(y_test, pred)\n",
        "  accuracy=accuracy_score(y_test, pred)\n",
        "  precision=precision_score(y_test, pred)\n",
        "  recall=recall_score(y_test, pred)\n",
        "  f1=f1_score(y_test, pred)\n",
        "\n",
        "  #ROC-AUC추가\n",
        "  roc_auc=roc_auc_score(y_test, pred_proba)\n",
        "  print('오차 행렬')\n",
        "  print(confusion)\n",
        "  #ROC-AUC print 추가\n",
        "  print('정확도: {0:.4f}, 정밀도: {1:.4f}, 재현율: {2:.4f}, F1: {3:.4f}'.format(accuracy, precision, recall, f1, roc_auc))"
      ],
      "metadata": {
        "id": "M2T1PNySs1JG"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "NuZXMhc7s1NU"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "bBimcm-47XdF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "e2JBOPvL7Xvs"
      }
    }
  ]
}