{"id":1002247,"date":"2024-12-27T10:07:03","date_gmt":"2024-12-27T02:07:03","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1002247.html"},"modified":"2024-12-27T10:07:05","modified_gmt":"2024-12-27T02:07:05","slug":"python-%e5%86%b3%e7%ad%96%e6%a0%91-%e5%a6%82%e4%bd%95","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1002247.html","title":{"rendered":"python \u51b3\u7b56\u6811 \u5982\u4f55"},"content":{"rendered":"

\"python<\/p>\n

\u5728Python\u4e2d\u5b9e\u73b0\u51b3\u7b56\u6811\uff0c\u9996\u5148\u9700\u8981\u4e86\u89e3\u5176\u57fa\u7840\u6982\u5ff5\u3001\u5b89\u88c5\u5fc5\u8981\u7684\u5e93\u3001\u51c6\u5907\u6570\u636e\u3001\u6784\u5efa\u6a21\u578b\u3001\u8fdb\u884c\u8bad\u7ec3\u548c\u8bc4\u4f30\u3001\u53ef\u89c6\u5316\u7ed3\u679c\u3001\u4f18\u5316\u6a21\u578b\u53c2\u6570\u4ee5\u53ca\u5e94\u7528\u4e8e\u5b9e\u9645\u95ee\u9898\u3002<\/strong> \u51b3\u7b56\u6811\u662f\u4e00\u79cd\u7528\u4e8e\u5206\u7c7b\u548c\u56de\u5f52\u7684\u76d1\u7763\u5b66\u4e60\u65b9\u6cd5\uff0c\u5176\u901a\u8fc7\u5c06\u6570\u636e\u5206\u5272\u6210\u4e0d\u540c\u7684\u5b50\u96c6\uff0c\u6700\u7ec8\u5f62\u6210\u4e00\u68f5\u6811\u72b6\u7ed3\u6784\u6765\u8fdb\u884c\u51b3\u7b56\u3002\u4ee5\u4e0b\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u5728Python\u4e2d\u5b9e\u73b0\u51b3\u7b56\u6811\uff0c\u4ee5\u53ca\u5728\u8fc7\u7a0b\u4e2d\u9700\u8981\u6ce8\u610f\u7684\u7ec6\u8282\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u51b3\u7b56\u6811\u7684\u57fa\u7840\u6982\u5ff5<\/h3>\n<\/p>\n

\u51b3\u7b56\u6811\u662f\u4e00\u79cd\u5e38\u7528\u4e8e\u5206\u7c7b\u548c\u56de\u5f52\u5206\u6790\u7684\u6a21\u578b\uff0c\u5176\u901a\u8fc7\u4e00\u7cfb\u5217\u7684\u201c\u662f\u201d\u6216\u201c\u5426\u201d\u95ee\u9898\u5c06\u6570\u636e\u5206\u5272\u4e3a\u8d8a\u6765\u8d8a\u5c0f\u7684\u90e8\u5206\uff0c\u6700\u7ec8\u5f62\u6210\u4e00\u4e2a\u7c7b\u4f3c\u6811\u7684\u7ed3\u6784\u3002\u51b3\u7b56\u6811\u7684\u4e3b\u8981\u4f18\u70b9\u5305\u62ec\u7b80\u5355\u76f4\u89c2\u3001\u5bb9\u6613\u89e3\u91ca\u3001\u65e0\u9700\u5927\u91cf\u7684\u6570\u636e\u9884\u5904\u7406\u3001\u9002\u7528\u4e8e\u6570\u503c\u578b\u548c\u5206\u7c7b\u578b\u6570\u636e<\/strong>\u3002\u7136\u800c\uff0c\u51b3\u7b56\u6811\u4e5f\u6709\u4e00\u4e9b\u7f3a\u70b9\uff0c\u4f8b\u5982\u5bb9\u6613\u8fc7\u62df\u5408\u3001\u5bf9\u566a\u58f0\u654f\u611f\u7b49\u3002<\/p>\n<\/p>\n

    \n
  1. \u8282\u70b9\u548c\u53f6\u5b50\u8282\u70b9<\/strong>\uff1a\u5728\u51b3\u7b56\u6811\u4e2d\uff0c\u8282\u70b9\u4ee3\u8868\u7279\u5f81\uff0c\u53f6\u5b50\u8282\u70b9\u4ee3\u8868\u51b3\u7b56\u7ed3\u679c\u3002<\/li>\n
  2. \u4fe1\u606f\u589e\u76ca\u548c\u57fa\u5c3c\u4e0d\u7eaf\u5ea6<\/strong>\uff1a\u8fd9\u4e9b\u662f\u7528\u6765\u8861\u91cf\u51b3\u7b56\u6811\u5206\u5272\u8d28\u91cf\u7684\u6307\u6807\u3002\u4fe1\u606f\u589e\u76ca\u7528\u4e8e\u9009\u62e9\u6bcf\u6b21\u5206\u5272\u6570\u636e\u7684\u7279\u5f81\uff0c\u57fa\u5c3c\u4e0d\u7eaf\u5ea6\u5219\u7528\u4e8e\u8861\u91cf\u5206\u7c7b\u95ee\u9898\u4e2d\u7684\u4e0d\u7eaf\u5ea6\u3002<\/li>\n
  3. \u526a\u679d\u7b56\u7565<\/strong>\uff1a\u4e3a\u4e86\u9632\u6b62\u8fc7\u62df\u5408\uff0c\u9700\u8981\u5bf9\u751f\u6210\u7684\u51b3\u7b56\u6811\u8fdb\u884c\u526a\u679d\u3002\u526a\u679d\u53ef\u4ee5\u901a\u8fc7\u9650\u5236\u6811\u7684\u6700\u5927\u6df1\u5ea6\u6216\u6700\u5c0f\u6837\u672c\u6570\u6765\u5b9e\u73b0\u3002<\/li>\n<\/ol>\n

    \u4e8c\u3001\u5b89\u88c5\u5fc5\u8981\u7684\u5e93<\/h3>\n<\/p>\n

    \u5728Python\u4e2d\uff0c\u5b9e\u73b0\u51b3\u7b56\u6811\u6700\u5e38\u7528\u7684\u5e93\u662fScikit-learn\u3002\u9996\u5148\u9700\u8981\u786e\u4fdd\u5df2\u5b89\u88c5\u6b64\u5e93\uff1a<\/p>\n<\/p>\n

    pip install scikit-learn<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    Scikit-learn\u63d0\u4f9b\u4e86\u7b80\u5355\u6613\u7528\u7684\u63a5\u53e3\u6765\u6784\u5efa\u548c\u8bad\u7ec3\u51b3\u7b56\u6811\u6a21\u578b\u3002\u6b64\u5916\uff0c\u8fd8\u53ef\u4ee5\u501f\u52a9\u5176\u4ed6\u5e93\u5982Pandas\u548cMatplotlib\u6765\u5904\u7406\u6570\u636e\u548c\u53ef\u89c6\u5316\u7ed3\u679c\u3002<\/p>\n<\/p>\n
    \u4e09\u3001\u51c6\u5907\u6570\u636e<\/h3>\n<\/p>\n
    \u5728\u6784\u5efa\u51b3\u7b56\u6811\u6a21\u578b\u4e4b\u524d\uff0c\u9700\u8981\u51c6\u5907\u597d\u6570\u636e\u3002\u8fd9\u901a\u5e38\u5305\u62ec\u6570\u636e\u7684\u6536\u96c6\u3001\u6e05\u6d17\u3001\u9884\u5904\u7406\u548c\u5212\u5206\u3002<\/p>\n<\/p>\n
      \n
    1. \u6570\u636e\u6536\u96c6\u548c\u6e05\u6d17<\/strong>\uff1a\u5728\u6536\u96c6\u6570\u636e\u540e\uff0c\u68c0\u67e5\u5e76\u5904\u7406\u7f3a\u5931\u503c\u3001\u5f02\u5e38\u503c\u548c\u91cd\u590d\u6570\u636e\u3002<\/li>\n
    2. \u7279\u5f81\u9009\u62e9\u548c\u63d0\u53d6<\/strong>\uff1a\u9009\u62e9\u5bf9\u6a21\u578b\u6709\u7528\u7684\u7279\u5f81\uff0c\u5e76\u8fdb\u884c\u5fc5\u8981\u7684\u7279\u5f81\u63d0\u53d6\u548c\u8f6c\u6362\u3002<\/li>\n
    3. \u6570\u636e\u5212\u5206<\/strong>\uff1a\u901a\u5e38\u5c06\u6570\u636e\u5212\u5206\u4e3a\u8bad\u7ec3\u96c6\u3001\u9a8c\u8bc1\u96c6\u548c\u6d4b\u8bd5\u96c6\uff0c\u4ee5\u4fbf\u8fdb\u884c\u6a21\u578b\u8bad\u7ec3\u548c\u8bc4\u4f30\u3002<\/li>\n<\/ol>\n
      import pandas as pd<\/p>\n
      \u793a\u4f8b\u6570\u636e\u96c6\u52a0\u8f7d<\/strong><\/h2>\n
      data = pd.read_csv('your_dataset.csv')<\/p>\n
      \u6570\u636e\u6e05\u6d17\u548c\u9884\u5904\u7406<\/strong><\/h2>\n
      data = data.dropna()<\/p>\n
      <\/code><\/pre>\n<\/p>\n
      \u56db\u3001\u6784\u5efa\u6a21\u578b<\/h3>\n<\/p>\n
      \u4f7f\u7528Scikit-learn\u4e2d\u7684DecisionTreeClassifier<\/code>\u6216DecisionTreeRegressor<\/code>\u6765\u6784\u5efa\u51b3\u7b56\u6811\u6a21\u578b\u3002\u9700\u8981\u6839\u636e\u95ee\u9898\u7684\u6027\u8d28\u9009\u62e9\u5408\u9002\u7684\u6a21\u578b\u3002<\/p>\n<\/p>\n
        \n
      1. \u6a21\u578b\u521d\u59cb\u5316<\/strong>\uff1a\u8bbe\u7f6e\u51b3\u7b56\u6811\u7684\u53c2\u6570\uff0c\u5982\u6700\u5927\u6df1\u5ea6\u3001\u6700\u5c0f\u6837\u672c\u5206\u88c2\u6570\u7b49\u3002<\/li>\n<\/ol>\n
        from sklearn.tree import DecisionTreeClassifier<\/p>\n
        \u521d\u59cb\u5316\u51b3\u7b56\u6811\u5206\u7c7b\u5668<\/strong><\/h2>\n
        model = DecisionTreeClassifier(max_depth=5, random_state=42)<\/p>\n
        <\/code><\/pre>\n<\/p>\n
          \n
        1. \u8bad\u7ec3\u6a21\u578b<\/strong>\uff1a\u5c06\u6570\u636e\u8f93\u5165\u6a21\u578b\u8fdb\u884c\u8bad\u7ec3\u3002<\/li>\n<\/ol>\n
          # \u7279\u5f81\u548c\u6807\u7b7e<\/p>\n
          X = data.drop('target', axis=1)<\/p>\n
          y = data['target']<\/p>\n
          \u8bad\u7ec3\u6a21\u578b<\/strong><\/h2>\n
          model.fit(X, y)<\/p>\n
          <\/code><\/pre>\n<\/p>\n
          \u4e94\u3001\u6a21\u578b\u8bc4\u4f30<\/h3>\n<\/p>\n
          \u8bc4\u4f30\u51b3\u7b56\u6811\u6a21\u578b\u7684\u6027\u80fd\u901a\u5e38\u4f7f\u7528\u51c6\u786e\u7387\u3001\u7cbe\u786e\u7387\u3001\u53ec\u56de\u7387\u3001F1\u5206\u6570\u7b49\u6307\u6807\u3002<\/p>\n<\/p>\n
            \n
          1. \u9884\u6d4b\u548c\u8bc4\u4f30<\/strong>\uff1a\u4f7f\u7528\u6d4b\u8bd5\u96c6\u8fdb\u884c\u9884\u6d4b\uff0c\u5e76\u901a\u8fc7\u6df7\u6dc6\u77e9\u9635\u548c\u5206\u7c7b\u62a5\u544a\u7b49\u65b9\u5f0f\u8bc4\u4f30\u6a21\u578b\u6027\u80fd\u3002<\/li>\n<\/ol>\n
            from sklearn.metrics import classification_report, confusion_matrix<\/p>\n
            \u6a21\u578b\u9884\u6d4b<\/strong><\/h2>\n
            predictions = model.predict(X_test)<\/p>\n
            \u8bc4\u4f30\u6a21\u578b<\/strong><\/h2>\n
            print(confusion_matrix(y_test, predictions))<\/p>\n
            print(classification_report(y_test, predictions))<\/p>\n
            <\/code><\/pre>\n<\/p>\n
              \n
            1. \u4ea4\u53c9\u9a8c\u8bc1<\/strong>\uff1a\u901a\u8fc7\u4ea4\u53c9\u9a8c\u8bc1\u6765\u8bc4\u4f30\u6a21\u578b\u7684\u6cdb\u5316\u6027\u80fd\u3002<\/li>\n<\/ol>\n
              from sklearn.model_selection import cross_val_score<\/p>\n
              \u4ea4\u53c9\u9a8c\u8bc1<\/strong><\/h2>\n
              scores = cross_val_score(model, X, y, cv=5)<\/p>\n
              print(f"Cross-validation scores: {scores}")<\/p>\n
              <\/code><\/pre>\n<\/p>\n
              \u516d\u3001\u53ef\u89c6\u5316\u7ed3\u679c<\/h3>\n<\/p>\n
              \u53ef\u89c6\u5316\u51b3\u7b56\u6811\u53ef\u4ee5\u5e2e\u52a9\u7406\u89e3\u6a21\u578b\u7684\u51b3\u7b56\u8fc7\u7a0b\u3002Scikit-learn\u63d0\u4f9b\u4e86\u7b80\u5355\u7684\u63a5\u53e3\u6765\u7ed8\u5236\u51b3\u7b56\u6811\u3002<\/p>\n<\/p>\n
                \n
              1. \u7ed8\u5236\u51b3\u7b56\u6811<\/strong>\uff1a\u4f7f\u7528plot_tree<\/code>\u51fd\u6570\u6765\u53ef\u89c6\u5316\u51b3\u7b56\u6811\u7ed3\u6784\u3002<\/li>\n<\/ol>\n
                from sklearn.tree import plot_tree<\/p>\n
                import matplotlib.pyplot as plt<\/p>\n
                \u7ed8\u5236\u51b3\u7b56\u6811<\/strong><\/h2>\n
                plt.figure(figsize=(20, 10))<\/p>\n
                plot_tree(model, filled=True, feature_names=X.columns, class_names=True)<\/p>\n
                plt.show()<\/p>\n
                <\/code><\/pre>\n<\/p>\n
                  \n
                1. \u7279\u5f81\u91cd\u8981\u6027<\/strong>\uff1a\u901a\u8fc7\u6a21\u578b\u7684feature_importances_<\/code>\u5c5e\u6027\u83b7\u53d6\u7279\u5f81\u7684\u91cd\u8981\u6027\u3002<\/li>\n<\/ol>\n
                  # \u8f93\u51fa\u7279\u5f81\u91cd\u8981\u6027<\/p>\n
                  importances = model.feature_importances_<\/p>\n
                  feature_importance_dict = {name: importance for name, importance in zip(X.columns, importances)}<\/p>\n
                  print(feature_importance_dict)<\/p>\n
                  <\/code><\/pre>\n<\/p>\n
                  \u4e03\u3001\u4f18\u5316\u6a21\u578b\u53c2\u6570<\/h3>\n<\/p>\n
                  \u4e3a\u4e86\u63d0\u5347\u6a21\u578b\u6027\u80fd\uff0c\u53ef\u4ee5\u901a\u8fc7\u7f51\u683c\u641c\u7d22\u6216\u968f\u673a\u641c\u7d22\u6765\u4f18\u5316\u51b3\u7b56\u6811\u7684\u53c2\u6570\u3002<\/p>\n<\/p>\n
                    \n
                  1. \u7f51\u683c\u641c\u7d22<\/strong>\uff1a\u901a\u8fc7\u904d\u5386\u53c2\u6570\u7684\u6240\u6709\u53ef\u80fd\u7ec4\u5408\u6765\u627e\u5230\u6700\u4f73\u53c2\u6570\u3002<\/li>\n<\/ol>\n
                    from sklearn.model_selection import GridSearchCV<\/p>\n
                    \u5b9a\u4e49\u53c2\u6570\u7f51\u683c<\/strong><\/h2>\n
                    param_grid = {<\/p>\n
                        'max_depth': [3, 5, 7, 10],<\/p>\n
                        'min_samples_split': [2, 5, 10]<\/p>\n
                    }<\/p>\n
                    \u7f51\u683c\u641c\u7d22<\/strong><\/h2>\n
                    grid_search = GridSearchCV(DecisionTreeClassifier(), param_grid, cv=5)<\/p>\n
                    grid_search.fit(X, y)<\/p>\n
                    print(f"Best parameters: {grid_search.best_params_}")<\/p>\n
                    <\/code><\/pre>\n<\/p>\n
                      \n
                    1. \u968f\u673a\u641c\u7d22<\/strong>\uff1a\u4e0e\u7f51\u683c\u641c\u7d22\u7c7b\u4f3c\uff0c\u4f46\u53ea\u968f\u673a\u9009\u62e9\u90e8\u5206\u53c2\u6570\u7ec4\u5408\u8fdb\u884c\u8bc4\u4f30\u3002<\/li>\n<\/ol>\n
                      from sklearn.model_selection import RandomizedSearchCV<\/p>\n
                      \u968f\u673a\u641c\u7d22<\/strong><\/h2>\n
                      random_search = RandomizedSearchCV(DecisionTreeClassifier(), param_grid, n_iter=10, cv=5)<\/p>\n
                      random_search.fit(X, y)<\/p>\n
                      print(f"Best parameters: {random_search.best_params_}")<\/p>\n
                      <\/code><\/pre>\n<\/p>\n
                      \u516b\u3001\u5e94\u7528\u4e8e\u5b9e\u9645\u95ee\u9898<\/h3>\n<\/p>\n
                      \u51b3\u7b56\u6811\u53ef\u4ee5\u5e94\u7528\u4e8e\u5404\u79cd\u5b9e\u9645\u95ee\u9898\uff0c\u5982\u4fe1\u7528\u98ce\u9669\u8bc4\u4f30\u3001\u5ba2\u6237\u7ec6\u5206\u3001\u75be\u75c5\u8bca\u65ad\u7b49\u3002\u5728\u5e94\u7528\u8fc7\u7a0b\u4e2d\uff0c\u9700\u8981\u6ce8\u610f\u6a21\u578b\u7684\u89e3\u91ca\u6027\u548c\u53ef\u7528\u6027\uff0c\u5e76\u6839\u636e\u5177\u4f53\u95ee\u9898\u8fdb\u884c\u8c03\u6574\u3002<\/p>\n<\/p>\n
                        \n
                      1. \u95ee\u9898\u5b9a\u4e49\u548c\u6570\u636e\u6536\u96c6<\/strong>\uff1a\u660e\u786e\u95ee\u9898\u5e76\u6536\u96c6\u76f8\u5173\u6570\u636e\u3002<\/li>\n
                      2. \u6a21\u578b\u6784\u5efa\u548c\u4f18\u5316<\/strong>\uff1a\u6784\u5efa\u9002\u5408\u95ee\u9898\u7684\u6570\u636e\u6a21\u578b\uff0c\u5e76\u901a\u8fc7\u8c03\u6574\u53c2\u6570\u4f18\u5316\u6a21\u578b\u6027\u80fd\u3002<\/li>\n
                      3. \u7ed3\u679c\u89e3\u91ca\u548c\u5e94\u7528<\/strong>\uff1a\u89e3\u91ca\u6a21\u578b\u7ed3\u679c\u5e76\u5e94\u7528\u4e8e\u51b3\u7b56\u652f\u6301\u3002<\/li>\n<\/ol>\n
                        \u901a\u8fc7\u4ee5\u4e0a\u6b65\u9aa4\uff0c\u60a8\u53ef\u4ee5\u5728Python\u4e2d\u6210\u529f\u5b9e\u73b0\u5e76\u5e94\u7528\u51b3\u7b56\u6811\u3002\u9700\u8981\u6ce8\u610f\u7684\u662f\uff0c\u51b3\u7b56\u6811\u7684\u6210\u529f\u5e94\u7528\u8fd8\u4f9d\u8d56\u4e8e\u5bf9\u6570\u636e\u7684\u6df1\u5165\u7406\u89e3\u548c\u5bf9\u6a21\u578b\u7684\u5408\u7406\u9009\u62e9\u3002<\/p>\n<\/p>\n
                        \u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
                         \u4ec0\u4e48\u662fPython\u51b3\u7b56\u6811\uff0c\u5982\u4f55\u4f7f\u7528\u5b83\u4eec\u8fdb\u884c\u5206\u7c7b\u548c\u56de\u5f52\uff1f<\/strong>
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