{"id":948938,"date":"2024-12-27T00:08:13","date_gmt":"2024-12-26T16:08:13","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/948938.html"},"modified":"2024-12-27T00:08:15","modified_gmt":"2024-12-26T16:08:15","slug":"python-%e5%a6%82%e4%bd%95%e5%ae%9e%e7%8e%b0%e5%88%86%e7%b1%bb","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/948938.html","title":{"rendered":"python \u5982\u4f55\u5b9e\u73b0\u5206\u7c7b"},"content":{"rendered":"

\"python<\/p>\n

Python\u5b9e\u73b0\u5206\u7c7b\u7684\u65b9\u5f0f\u6709\u591a\u79cd\uff0c\u5305\u62ec\u4f7f\u7528\u673a\u5668\u5b66\u4e60<\/a>\u5e93\u5982scikit-learn\u3001\u795e\u7ecf\u7f51\u7edc\u5e93\u5982TensorFlow\u6216PyTorch\uff0c\u4ee5\u53ca\u81ea\u7136\u8bed\u8a00\u5904\u7406\u5e93\u5982NLTK\u6216spaCy\u7b49\u3002\u9009\u62e9\u5408\u9002\u7684\u65b9\u6cd5\u53d6\u51b3\u4e8e\u5177\u4f53\u7684\u5e94\u7528\u573a\u666f\u3001\u6570\u636e\u7c7b\u578b\u548c\u9879\u76ee\u9700\u6c42\u3002\u672c\u6587\u5c06\u91cd\u70b9\u4ecb\u7ecd\u901a\u8fc7scikit-learn\u5b9e\u73b0\u5206\u7c7b\u7684\u57fa\u672c\u6b65\u9aa4\u3001\u5982\u4f55\u8fdb\u884c\u6570\u636e\u9884\u5904\u7406\u3001\u9009\u62e9\u5408\u9002\u7684\u5206\u7c7b\u5668\uff0c\u4ee5\u53ca\u5982\u4f55\u8bc4\u4f30\u6a21\u578b\u6027\u80fd\u3002<\/strong><\/p>\n<\/p>\n

\u4e00\u3001\u6570\u636e\u9884\u5904\u7406<\/p>\n<\/p>\n

\u5728\u8fdb\u884c\u5206\u7c7b\u4e4b\u524d\uff0c\u6570\u636e\u9884\u5904\u7406\u662f\u4e00\u4e2a\u81f3\u5173\u91cd\u8981\u7684\u6b65\u9aa4\u3002\u6570\u636e\u9884\u5904\u7406\u5305\u62ec\u6570\u636e\u6e05\u6d17\u3001\u7279\u5f81\u9009\u62e9\u548c\u7279\u5f81\u5de5\u7a0b\u7b49\u3002<\/p>\n<\/p>\n

    \n
  1. \u6570\u636e\u6e05\u6d17<\/strong><\/li>\n<\/ol>\n

    \u6570\u636e\u6e05\u6d17\u662f\u6307\u5904\u7406\u7f3a\u5931\u503c\u3001\u5f02\u5e38\u503c\u548c\u91cd\u590d\u6570\u636e\u7b49\u3002\u7f3a\u5931\u503c\u53ef\u4ee5\u901a\u8fc7\u5220\u9664\u3001\u63d2\u503c\u6216\u586b\u5145\u7b49\u65b9\u6cd5\u5904\u7406\uff0c\u800c\u5f02\u5e38\u503c\u53ef\u4ee5\u901a\u8fc7\u7edf\u8ba1\u65b9\u6cd5\u6216\u673a\u5668\u5b66\u4e60\u7b97\u6cd5\u8fdb\u884c\u68c0\u6d4b\u548c\u5904\u7406\u3002<\/p>\n<\/p>\n

    import pandas as pd<\/p>\n
    \u8bfb\u53d6\u6570\u636e<\/strong><\/h2>\n
    data = pd.read_csv('data.csv')<\/p>\n
    \u67e5\u770b\u7f3a\u5931\u503c<\/strong><\/h2>\n
    print(data.isnull().sum())<\/p>\n
    \u586b\u5145\u7f3a\u5931\u503c<\/strong><\/h2>\n
    data.fillna(method='ffill', inplace=True)<\/p>\n
    <\/code><\/pre>\n<\/p>\n
      \n
    1. \u7279\u5f81\u9009\u62e9<\/strong><\/li>\n<\/ol>\n
      \u7279\u5f81\u9009\u62e9\u662f\u6307\u4ece\u539f\u59cb\u6570\u636e\u4e2d\u9009\u62e9\u5bf9\u5206\u7c7b\u4efb\u52a1\u6709\u7528\u7684\u7279\u5f81\u3002\u8fd9\u53ef\u4ee5\u901a\u8fc7\u7edf\u8ba1\u65b9\u6cd5\u6216\u7b97\u6cd5\u5982\u9012\u5f52\u7279\u5f81\u6d88\u9664\uff08RFE\uff09\u548c\u4e3b\u6210\u5206\u5206\u6790\uff08PCA\uff09\u7b49\u5b9e\u73b0\u3002<\/p>\n<\/p>\n
      from sklearn.feature_selection import RFE<\/p>\n
      from sklearn.linear_model import LogisticRegression<\/p>\n
      \u521d\u59cb\u5316\u6a21\u578b<\/strong><\/h2>\n
      model = LogisticRegression()<\/p>\n
      RFE\u8fdb\u884c\u7279\u5f81\u9009\u62e9<\/strong><\/h2>\n
      rfe = RFE(model, 3)<\/p>\n
      fit = rfe.fit(data.iloc[:, :-1], data.iloc[:, -1])<\/p>\n
      print("Selected Features: %s" % fit.support_)<\/p>\n
      print("Feature Ranking: %s" % fit.ranking_)<\/p>\n
      <\/code><\/pre>\n<\/p>\n
        \n
      1. \u7279\u5f81\u5de5\u7a0b<\/strong><\/li>\n<\/ol>\n
        \u7279\u5f81\u5de5\u7a0b\u662f\u6307\u901a\u8fc7\u5bf9\u539f\u59cb\u6570\u636e\u8fdb\u884c\u8f6c\u6362\u548c\u7ec4\u5408\uff0c\u521b\u5efa\u65b0\u7684\u7279\u5f81\u3002\u5e38\u7528\u7684\u65b9\u6cd5\u5305\u62ec\u6807\u51c6\u5316\u3001\u5f52\u4e00\u5316\u3001\u7f16\u7801\u548c\u964d\u7ef4\u7b49\u3002<\/p>\n<\/p>\n
        from sklearn.preprocessing import StandardScaler<\/p>\n
        \u6807\u51c6\u5316\u7279\u5f81<\/strong><\/h2>\n
        scaler = StandardScaler()<\/p>\n
        data_scaled = scaler.fit_transform(data)<\/p>\n
        <\/code><\/pre>\n<\/p>\n
        \u4e8c\u3001\u9009\u62e9\u5408\u9002\u7684\u5206\u7c7b\u5668<\/p>\n<\/p>\n
        \u6839\u636e\u6570\u636e\u7684\u7279\u6027\u548c\u4efb\u52a1\u9700\u6c42\uff0c\u9009\u62e9\u5408\u9002\u7684\u5206\u7c7b\u5668\u662f\u4fdd\u8bc1\u6a21\u578b\u6027\u80fd\u7684\u5173\u952e\u6b65\u9aa4\u3002\u5e38\u7528\u7684\u5206\u7c7b\u5668\u5305\u62ec\u903b\u8f91\u56de\u5f52\u3001\u652f\u6301\u5411\u91cf\u673a\uff08SVM\uff09\u3001\u51b3\u7b56\u6811\u3001\u968f\u673a\u68ee\u6797\u548cK\u8fd1\u90bb\uff08KNN\uff09\u7b49\u3002<\/p>\n<\/p>\n
          \n
        1. \u903b\u8f91\u56de\u5f52<\/strong><\/li>\n<\/ol>\n
          \u903b\u8f91\u56de\u5f52\u662f\u4e00\u79cd\u7ebf\u6027\u5206\u7c7b\u7b97\u6cd5\uff0c\u9002\u7528\u4e8e\u4e8c\u5206\u7c7b\u95ee\u9898\u3002\u5b83\u901a\u8fc7\u6700\u5927\u5316\u4f3c\u7136\u51fd\u6570\u6765\u4f30\u8ba1\u53c2\u6570\u3002<\/p>\n<\/p>\n
          from sklearn.linear_model import LogisticRegression<\/p>\n
          from sklearn.model_selection import trAI<\/a>n_test_split<\/p>\n
          from sklearn.metrics import accuracy_score<\/p>\n
          \u5212\u5206\u6570\u636e\u96c6<\/strong><\/h2>\n
          X_train, X_test, y_train, y_test = train_test_split(data_scaled, data.iloc[:, -1], test_size=0.2, random_state=42)<\/p>\n
          \u521d\u59cb\u5316\u6a21\u578b<\/strong><\/h2>\n
          logreg = LogisticRegression()<\/p>\n
          \u8bad\u7ec3\u6a21\u578b<\/strong><\/h2>\n
          logreg.fit(X_train, y_train)<\/p>\n
          \u9884\u6d4b<\/strong><\/h2>\n
          y_pred = logreg.predict(X_test)<\/p>\n
          \u8bc4\u4f30<\/strong><\/h2>\n
          print("Accuracy:", accuracy_score(y_test, y_pred))<\/p>\n
          <\/code><\/pre>\n<\/p>\n
            \n
          1. \u652f\u6301\u5411\u91cf\u673a<\/strong><\/li>\n<\/ol>\n
            \u652f\u6301\u5411\u91cf\u673a\uff08SVM\uff09\u662f\u4e00\u79cd\u5f3a\u5927\u7684\u5206\u7c7b\u5668\uff0c\u5c24\u5176\u9002\u7528\u4e8e\u9ad8\u7ef4\u6570\u636e\u3002\u5b83\u901a\u8fc7\u6784\u9020\u4e00\u4e2a\u8d85\u5e73\u9762\u6765\u8fdb\u884c\u5206\u7c7b\u3002<\/p>\n<\/p>\n
            from sklearn.svm import SVC<\/p>\n
            \u521d\u59cb\u5316\u6a21\u578b<\/strong><\/h2>\n
            svm = SVC()<\/p>\n
            \u8bad\u7ec3\u6a21\u578b<\/strong><\/h2>\n
            svm.fit(X_train, y_train)<\/p>\n
            \u9884\u6d4b<\/strong><\/h2>\n
            y_pred = svm.predict(X_test)<\/p>\n
            \u8bc4\u4f30<\/strong><\/h2>\n
            print("Accuracy:", accuracy_score(y_test, y_pred))<\/p>\n
            <\/code><\/pre>\n<\/p>\n
              \n
            1. \u51b3\u7b56\u6811<\/strong><\/li>\n<\/ol>\n
              \u51b3\u7b56\u6811\u662f\u4e00\u79cd\u975e\u53c2\u6570\u7684\u76d1\u7763\u5b66\u4e60\u65b9\u6cd5\uff0c\u53ef\u4ee5\u7528\u4e8e\u5206\u7c7b\u548c\u56de\u5f52\u3002\u5b83\u901a\u8fc7\u5b66\u4e60\u7b80\u5355\u7684\u51b3\u7b56\u89c4\u5219\uff08\u5982if-else\uff09\u4ece\u6570\u636e\u4e2d\u63a8\u65ad\u51fa\u6709\u610f\u4e49\u7684\u6a21\u5f0f\u3002<\/p>\n<\/p>\n
              from sklearn.tree import DecisionTreeClassifier<\/p>\n
              \u521d\u59cb\u5316\u6a21\u578b<\/strong><\/h2>\n
              tree = DecisionTreeClassifier()<\/p>\n
              \u8bad\u7ec3\u6a21\u578b<\/strong><\/h2>\n
              tree.fit(X_train, y_train)<\/p>\n
              \u9884\u6d4b<\/strong><\/h2>\n
              y_pred = tree.predict(X_test)<\/p>\n
              \u8bc4\u4f30<\/strong><\/h2>\n
              print("Accuracy:", accuracy_score(y_test, y_pred))<\/p>\n
              <\/code><\/pre>\n<\/p>\n
              \u4e09\u3001\u6a21\u578b\u8bc4\u4f30<\/p>\n<\/p>\n
              \u6a21\u578b\u8bc4\u4f30\u662f\u9a8c\u8bc1\u5206\u7c7b\u5668\u6027\u80fd\u7684\u5173\u952e\u6b65\u9aa4\u3002\u5e38\u7528\u7684\u8bc4\u4f30\u6307\u6807\u5305\u62ec\u51c6\u786e\u7387\u3001\u7cbe\u786e\u7387\u3001\u53ec\u56de\u7387\u3001F1\u5206\u6570\u548c\u6df7\u6dc6\u77e9\u9635\u7b49\u3002<\/p>\n<\/p>\n
                \n
              1. \u51c6\u786e\u7387<\/strong><\/li>\n<\/ol>\n
                \u51c6\u786e\u7387\u662f\u6307\u5206\u7c7b\u6b63\u786e\u7684\u6837\u672c\u6570\u91cf\u5360\u603b\u6837\u672c\u6570\u91cf\u7684\u6bd4\u4f8b\u3002<\/p>\n<\/p>\n
                from sklearn.metrics import accuracy_score<\/p>\n
                print("Accuracy:", accuracy_score(y_test, y_pred))<\/p>\n
                <\/code><\/pre>\n<\/p>\n
                  \n
                1. \u7cbe\u786e\u7387\u3001\u53ec\u56de\u7387\u548cF1\u5206\u6570<\/strong><\/li>\n<\/ol>\n
                  \u7cbe\u786e\u7387\u662f\u6307\u5206\u7c7b\u5668\u9884\u6d4b\u4e3a\u6b63\u7c7b\u7684\u6837\u672c\u4e2d\uff0c\u5b9e\u9645\u4e3a\u6b63\u7c7b\u7684\u6bd4\u4f8b\uff1b\u53ec\u56de\u7387\u662f\u6307\u5b9e\u9645\u4e3a\u6b63\u7c7b\u7684\u6837\u672c\u4e2d\uff0c\u5206\u7c7b\u5668\u9884\u6d4b\u4e3a\u6b63\u7c7b\u7684\u6bd4\u4f8b\uff1bF1\u5206\u6570\u662f\u7cbe\u786e\u7387\u548c\u53ec\u56de\u7387\u7684\u8c03\u548c\u5e73\u5747\u6570\u3002<\/p>\n<\/p>\n
                  from sklearn.metrics import precision_score, recall_score, f1_score<\/p>\n
                  print("Precision:", precision_score(y_test, y_pred, average='weighted'))<\/p>\n
                  print("Recall:", recall_score(y_test, y_pred, average='weighted'))<\/p>\n
                  print("F1 Score:", f1_score(y_test, y_pred, average='weighted'))<\/p>\n
                  <\/code><\/pre>\n<\/p>\n
                    \n
                  1. \u6df7\u6dc6\u77e9\u9635<\/strong><\/li>\n<\/ol>\n
                    \u6df7\u6dc6\u77e9\u9635\u7528\u4e8e\u63cf\u8ff0\u5206\u7c7b\u5668\u7684\u6027\u80fd\uff0c\u5b83\u663e\u793a\u4e86\u9884\u6d4b\u7c7b\u522b\u4e0e\u5b9e\u9645\u7c7b\u522b\u7684\u5bf9\u6bd4\u3002<\/p>\n<\/p>\n
                    from sklearn.metrics import confusion_matrix<\/p>\n
                    print("Confusion Matrix:\\n", confusion_matrix(y_test, y_pred))<\/p>\n
                    <\/code><\/pre>\n<\/p>\n
                    \u56db\u3001\u6a21\u578b\u4f18\u5316<\/p>\n<\/p>\n
                    \u4e3a\u4e86\u63d0\u9ad8\u5206\u7c7b\u5668\u7684\u6027\u80fd\uff0c\u53ef\u4ee5\u901a\u8fc7\u8c03\u6574\u8d85\u53c2\u6570\u3001\u7279\u5f81\u9009\u62e9\u548c\u96c6\u6210\u5b66\u4e60\u7b49\u65b9\u6cd5\u8fdb\u884c\u4f18\u5316\u3002<\/p>\n<\/p>\n
                      \n
                    1. \u8d85\u53c2\u6570\u8c03\u6574<\/strong><\/li>\n<\/ol>\n
                      \u8d85\u53c2\u6570\u8c03\u6574\u662f\u6307\u901a\u8fc7\u7f51\u683c\u641c\u7d22\u6216\u968f\u673a\u641c\u7d22\u7b49\u65b9\u6cd5\uff0c\u5bfb\u627e\u6700\u4f73\u7684\u8d85\u53c2\u6570\u7ec4\u5408\u3002<\/p>\n<\/p>\n
                      from sklearn.model_selection import GridSearchCV<\/p>\n
                      \u5b9a\u4e49\u53c2\u6570\u7f51\u683c<\/strong><\/h2>\n
                      param_grid = {'C': [0.1, 1, 10, 100], 'kernel': ['linear', 'rbf']}<\/p>\n
                      \u7f51\u683c\u641c\u7d22<\/strong><\/h2>\n
                      grid = GridSearchCV(SVC(), param_grid, refit=True, verbose=2)<\/p>\n
                      grid.fit(X_train, y_train)<\/p>\n
                      print("Best Parameters:", grid.best_params_)<\/p>\n
                      <\/code><\/pre>\n<\/p>\n
                        \n
                      1. \u7279\u5f81\u9009\u62e9<\/strong><\/li>\n<\/ol>\n
                        \u7279\u5f81\u9009\u62e9\u53ef\u4ee5\u901a\u8fc7\u8fc7\u6ee4\u6cd5\u3001\u5305\u88f9\u6cd5\u548c\u5d4c\u5165\u6cd5\u7b49\u5b9e\u73b0\u3002\u901a\u8fc7\u9009\u62e9\u91cd\u8981\u7279\u5f81\uff0c\u53ef\u4ee5\u63d0\u9ad8\u6a21\u578b\u7684\u6027\u80fd\u548c\u89e3\u91ca\u6027\u3002<\/p>\n<\/p>\n
                          \n
                        1. \u96c6\u6210\u5b66\u4e60<\/strong><\/li>\n<\/ol>\n
                          \u96c6\u6210\u5b66\u4e60\u901a\u8fc7\u7ec4\u5408\u591a\u4e2a\u5206\u7c7b\u5668\u7684\u9884\u6d4b\u7ed3\u679c\uff0c\u63d0\u9ad8\u6a21\u578b\u7684\u6cdb\u5316\u80fd\u529b\u3002\u5e38\u7528\u7684\u65b9\u6cd5\u5305\u62ec\u968f\u673a\u68ee\u6797\u3001Adaboost\u548cXGBoost\u7b49\u3002<\/p>\n<\/p>\n
                          from sklearn.ensemble import RandomForestClassifier<\/p>\n
                          \u521d\u59cb\u5316\u6a21\u578b<\/strong><\/h2>\n
                          rf = RandomForestClassifier(n_estimators=100)<\/p>\n
                          \u8bad\u7ec3\u6a21\u578b<\/strong><\/h2>\n
                          rf.fit(X_train, y_train)<\/p>\n
                          \u9884\u6d4b<\/strong><\/h2>\n
                          y_pred = rf.predict(X_test)<\/p>\n
                          \u8bc4\u4f30<\/strong><\/h2>\n
                          print("Accuracy:", accuracy_score(y_test, y_pred))<\/p>\n
                          <\/code><\/pre>\n<\/p>\n
                          \u4e94\u3001\u5b9e\u4f8b\u5e94\u7528<\/p>\n<\/p>\n
                          \u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0cPython\u5206\u7c7b\u7b97\u6cd5\u53ef\u4ee5\u5e94\u7528\u4e8e\u5404\u79cd\u573a\u666f\uff0c\u5982\u6587\u672c\u5206\u7c7b\u3001\u56fe\u50cf\u5206\u7c7b\u548c\u751f\u7269\u4fe1\u606f\u5b66\u7b49\u3002<\/p>\n<\/p>\n
                            \n
                          1. \u6587\u672c\u5206\u7c7b<\/strong><\/li>\n<\/ol>\n
                            \u6587\u672c\u5206\u7c7b\u662f\u5c06\u6587\u672c\u6570\u636e\u5206\u4e3a\u591a\u4e2a\u7c7b\u522b\u7684\u4efb\u52a1\u3002\u5e38\u7528\u7684\u65b9\u6cd5\u5305\u62ecTF-IDF\u7279\u5f81\u63d0\u53d6\u548c\u6734\u7d20\u8d1d\u53f6\u65af\u5206\u7c7b\u5668\u7b49\u3002<\/p>\n<\/p>\n
                            from sklearn.feature_extraction.text import TfidfVectorizer<\/p>\n
                            from sklearn.naive_bayes import MultinomialNB<\/p>\n
                            \u6587\u672c\u6570\u636e<\/strong><\/h2>\n
                            texts = ["I love programming.", "Python is great.", "I enjoy learning new things."]<\/p>\n
                            \u7279\u5f81\u63d0\u53d6<\/strong><\/h2>\n
                            vectorizer = TfidfVectorizer()<\/p>\n
                            X = vectorizer.fit_transform(texts)<\/p>\n
                            \u6807\u7b7e<\/strong><\/h2>\n
                            y = [1, 1, 0]<\/p>\n
                            \u521d\u59cb\u5316\u6a21\u578b<\/strong><\/h2>\n
                            nb = MultinomialNB()<\/p>\n
                            \u8bad\u7ec3\u6a21\u578b<\/strong><\/h2>\n
                            nb.fit(X, y)<\/p>\n
                            \u9884\u6d4b<\/strong><\/h2>\n
                            y_pred = nb.predict(X)<\/p>\n
                            print("Predicted Labels:", y_pred)<\/p>\n
                            <\/code><\/pre>\n<\/p>\n
                              \n
                            1. \u56fe\u50cf\u5206\u7c7b<\/strong><\/li>\n<\/ol>\n
                              \u56fe\u50cf\u5206\u7c7b\u662f\u5c06\u56fe\u50cf\u6570\u636e\u5206\u4e3a\u591a\u4e2a\u7c7b\u522b\u7684\u4efb\u52a1\u3002\u5e38\u7528\u7684\u65b9\u6cd5\u5305\u62ec\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\uff08CNN\uff09\u548c\u8fc1\u79fb\u5b66\u4e60\u7b49\u3002<\/p>\n<\/p>\n
                              from tensorflow.keras.applications import VGG16<\/p>\n
                              from tensorflow.keras.preprocessing.image import ImageDataGenerator<\/p>\n
                              from tensorflow.keras import layers, models<\/p>\n
                              \u52a0\u8f7d\u9884\u8bad\u7ec3\u6a21\u578b<\/strong><\/h2>\n
                              base_model = VGG16(weights='imagenet', include_top=False, input_shape=(224, 224, 3))<\/p>\n
                              \u51bb\u7ed3\u5377\u79ef\u57fa<\/strong><\/h2>\n
                              base_model.trainable = False<\/p>\n
                              \u6784\u5efa\u6a21\u578b<\/strong><\/h2>\n
                              model = models.Sequential()<\/p>\n
                              model.add(base_model)<\/p>\n
                              model.add(layers.Flatten())<\/p>\n
                              model.add(layers.Dense(256, activation='relu'))<\/p>\n
                              model.add(layers.Dense(1, activation='sigmoid'))<\/p>\n
                              \u7f16\u8bd1\u6a21\u578b<\/strong><\/h2>\n
                              model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])<\/p>\n
                              \u6570\u636e\u589e\u5f3a<\/strong><\/h2>\n
                              train_datagen = ImageDataGenerator(rescale=1.\/255)<\/p>\n
                              \u8bad\u7ec3\u6a21\u578b<\/strong><\/h2>\n
                              train_generator = train_datagen.flow_from_directory('data\/train', target_size=(224, 224), batch_size=20, class_mode='binary')<\/p>\n
                              model.fit(train_generator, epochs=10)<\/p>\n
                              <\/code><\/pre>\n<\/p>\n
                              \u901a\u8fc7\u4ee5\u4e0a\u4ecb\u7ecd\uff0c\u6211\u4eec\u53ef\u4ee5\u770b\u51fa\uff0cPython\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u5de5\u5177\u548c\u5e93\u6765\u5b9e\u73b0\u5206\u7c7b\u4efb\u52a1\u3002\u65e0\u8bba\u662f\u4f20\u7edf\u7684\u673a\u5668\u5b66\u4e60\u65b9\u6cd5\uff0c\u8fd8\u662f\u73b0\u4ee3\u7684\u6df1\u5ea6\u5b66\u4e60\u6280\u672f\uff0c\u90fd\u80fd\u5e2e\u52a9\u6211\u4eec\u5728\u5404\u7c7b\u5b9e\u9645\u5e94\u7528\u4e2d\u6784\u5efa\u51fa\u8272\u7684\u5206\u7c7b\u6a21\u578b\u3002\u9009\u62e9\u5408\u9002\u7684\u65b9\u6cd5\u548c\u5de5\u5177\uff0c\u4e0d\u4ec5\u53ef\u4ee5\u63d0\u9ad8\u6a21\u578b\u7684\u6027\u80fd\uff0c\u8fd8\u80fd\u4e3a\u540e\u7eed\u7684\u51b3\u7b56\u548c\u5206\u6790\u63d0\u4f9b\u6709\u529b\u652f\u6301\u3002<\/p>\n<\/p>\n
                              \u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
                               \u5982\u4f55\u9009\u62e9\u5408\u9002\u7684\u5206\u7c7b\u7b97\u6cd5\u6765\u5b9e\u73b0\u6211\u7684Python\u9879\u76ee\uff1f<\/strong>
                              \u5728\u9009\u62e9\u5206\u7c7b\u7b97\u6cd5\u65f6\uff0c\u9700\u8981\u8003\u8651\u6570\u636e\u96c6\u7684\u7279\u6027\u548c\u9879\u76ee\u7684\u9700\u6c42\u3002\u5e38\u89c1\u7684\u5206\u7c7b\u7b97\u6cd5\u5305\u62ec\u903b\u8f91\u56de\u5f52\u3001\u652f\u6301\u5411\u91cf\u673a\u3001\u51b3\u7b56\u6811\u548c\u968f\u673a\u68ee\u6797\u7b49\u3002\u903b\u8f91\u56de\u5f52\u9002\u5408\u4e8e\u7ebf\u6027\u53ef\u5206\u7684\u6570\u636e\u96c6\uff0c\u800c\u968f\u673a\u68ee\u6797\u5728\u5904\u7406\u590d\u6742\u7684\u6570\u636e\u96c6\u65f6\u8868\u73b0\u826f\u597d\u3002\u901a\u8fc7\u4ea4\u53c9\u9a8c\u8bc1\u548c\u8d85\u53c2\u6570\u8c03\u4f18\uff0c\u53ef\u4ee5\u627e\u5230\u6700\u9002\u5408\u60a8\u6570\u636e\u7684\u7b97\u6cd5\u3002<\/p>\n
                              \u5728Python\u4e2d\u5982\u4f55\u8bc4\u4f30\u5206\u7c7b\u6a21\u578b\u7684\u6027\u80fd\uff1f<\/strong>
                              \u8bc4\u4f30\u5206\u7c7b\u6a21\u578b\u7684\u6027\u80fd\u901a\u5e38\u4f7f\u7528\u6df7\u6dc6\u77e9\u9635\u3001\u7cbe\u786e\u7387\u3001\u53ec\u56de\u7387\u548cF1\u5206\u6570\u7b49\u6307\u6807\u3002\u53ef\u4ee5\u4f7f\u7528scikit-learn\u5e93\u4e2d\u7684classification_report<\/code>\u548cconfusion_matrix<\/code>\u6765\u8ba1\u7b97\u8fd9\u4e9b\u6307\u6807\u3002\u6b64\u5916\uff0cROC\u66f2\u7ebf\u548cAUC\u503c\u4e5f\u662f\u8861\u91cf\u6a21\u578b\u4f18\u52a3\u7684\u91cd\u8981\u5de5\u5177\u3002<\/p>\n
                              \u5982\u4f55\u5904\u7406\u5206\u7c7b\u4e2d\u7684\u4e0d\u5e73\u8861\u6570\u636e\u95ee\u9898\uff1f<\/strong>
                              \u4e0d\u5e73\u8861\u7684\u6570\u636e\u96c6\u53ef\u80fd\u4f1a\u5bfc\u81f4\u6a21\u578b\u504f\u5411\u4e8e\u591a\u6570\u7c7b\uff0c\u4ece\u800c\u5f71\u54cd\u5206\u7c7b\u6548\u679c\u3002\u53ef\u4ee5\u91c7\u7528\u51e0\u79cd\u65b9\u6cd5\u6765\u5904\u7406\u8fd9\u4e00\u95ee\u9898\uff0c\u5305\u62ec\u8fc7\u91c7\u6837\uff08\u5982SMOTE\uff09\u3001\u6b20\u91c7\u6837\u4ee5\u53ca\u4f7f\u7528\u52a0\u6743\u635f\u5931\u51fd\u6570\u7b49\u3002\u8fd9\u4e9b\u65b9\u6cd5\u80fd\u591f\u5e2e\u52a9\u6a21\u578b\u66f4\u597d\u5730\u5b66\u4e60\u5230\u5c11\u6570\u7c7b\u7684\u7279\u5f81\uff0c\u4ece\u800c\u63d0\u9ad8\u6574\u4f53\u5206\u7c7b\u6027\u80fd\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"Python\u5b9e\u73b0\u5206\u7c7b\u7684\u65b9\u5f0f\u6709\u591a\u79cd\uff0c\u5305\u62ec\u4f7f\u7528\u673a\u5668\u5b66\u4e60\u5e93\u5982scikit-learn\u3001\u795e\u7ecf\u7f51\u7edc\u5e93\u5982TensorFlo […]","protected":false},"author":3,"featured_media":948944,"comment_status":"closed","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[37],"tags":[],"acf":[],"_links":{"self":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/948938"}],"collection":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/comments?post=948938"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/948938\/revisions"}],"predecessor-version":[{"id":948947,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/948938\/revisions\/948947"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/948944"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=948938"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=948938"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=948938"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}