\n| \u8de8\u6a21\u6001\u6216\u6df1\u5ea6\u7279\u5f81<\/td>\n | \u6df1\u5ea6\u795e\u7ecf\u7f51\u7edc\uff08CNN\u3001Transformer\uff09+ Stacking<\/td>\n | \u5c06\u6df1\u5ea6\u7279\u5f81\u4f5c\u4e3a\u7b2c\u4e00\u5c42\u8f93\u5165\uff0c\u5143\u5b66\u4e60\u5668\u8fdb\u884c\u4e8c\u6b21\u878d\u5408<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n \u6a21\u578b\u6784\u5efa\u793a\u4f8b\uff08Python\uff09<\/strong><\/p>\nfrom<\/span> sklearn.<\/span>ensemble import<\/span> BaggingClassifier,<\/span> VotingClassifier,<\/span> StackingClassifier from<\/span> sklearn.<\/span>tree import<\/span> DecisionTreeClassifier from<\/span> sklearn.<\/span>linear_model import<\/span> LogisticRegression from<\/span> sklearn.<\/span>svm import<\/span> SVC from<\/span> xgboost import<\/span> XGBClassifier # Bagging \u793a\u4f8b\uff08\u57fa\u4e8e\u51b3\u7b56\u6811\uff09<\/span> bag =<\/span> BaggingClassifier(<\/span> base_estimator=<\/span>DecisionTreeClassifier(<\/span>max_depth=<\/span>5<\/span>)<\/span>,<\/span> n_estimators=<\/span>120<\/span>,<\/span> max_samples=<\/span>0.8<\/span>,<\/span> max_features=<\/span>0.8<\/span>,<\/span> random_state=<\/span>42<\/span>,<\/span> n_jobs=<\/span>-<\/span>1<\/span>)<\/span> # Boosting \u793a\u4f8b\uff08XGBoost\uff09<\/span> boost =<\/span> XGBClassifier(<\/span> n_estimators=<\/span>350<\/span>,<\/span> learning_rate=<\/span>0.04<\/span>,<\/span> max_depth=<\/span>7<\/span>,<\/span> subsample=<\/span>0.85<\/span>,<\/span> colsample_bytree=<\/span>0.85<\/span>,<\/span> objective=<\/span>'binary:logistic'<\/span>,<\/span> eval_metric=<\/span>'auc'<\/span>,<\/span> n_jobs=<\/span>-<\/span>1<\/span>,<\/span> random_state=<\/span>42<\/span>)<\/span> # Stacking \u793a\u4f8b\uff08\u5f02\u6784\u6a21\u578b\u878d\u5408\uff09<\/span> stack =<\/span> StackingClassifier(<\/span> estimators=<\/span>[<\/span> (<\/span>'dt'<\/span>,<\/span> DecisionTreeClassifier(<\/span>max_depth=<\/span>4<\/span>)<\/span>)<\/span>,<\/span> (<\/span>'svc'<\/span>,<\/span> SVC(<\/span>probability=<\/span>True<\/span>,<\/span> kernel=<\/span>'rbf'<\/span>)<\/span>)<\/span>,<\/span> (<\/span>'lr'<\/span>,<\/span> LogisticRegression(<\/span>max_iter=<\/span>500<\/span>)<\/span>)<\/span> ]<\/span>,<\/span> final_estimator=<\/span>LogisticRegression(<\/span>)<\/span>,<\/span> cv=<\/span>5<\/span>,<\/span> n_jobs=<\/span>-<\/span>1<\/span>)<\/span><\/code><\/pre>\n\u8d85\u53c2\u6570\u8c03\u4f18\u4e0e\u4ea4\u53c9\u9a8c\u8bc1<\/strong><\/p>\n\n- \u7f51\u683c\u641c\u7d22\uff08GridSearchCV\uff09\u00a0\u4e0e\u968f\u673a\u641c\u7d22\uff08RandomizedSearchCV\uff09\u200d \u662f\u8c03\u4f18\u7684\u5e38\u7528\u624b\u6bb5\u3002<\/li>\n
- \u5bf9 Boosting \u7cfb\u5217\u6a21\u578b\uff0c\u5efa\u8bae\u5148\u8c03\u5b66\u4e60\u7387\u4e0e\u6811\u7684\u6df1\u5ea6\uff0c\u518d\u9010\u6b65\u6269\u5927\u6811\u7684\u6570\u91cf\uff0c\u9632\u6b62\u8fc7\u62df\u5408\u3002<\/li>\n
- \u5bf9 Bagging \u4e0e\u968f\u673a\u68ee\u6797\uff0c\u91cd\u70b9\u5173\u6ce8\u00a0
max_features<\/code>\u3001max_samples<\/code><\/strong> \u4e0e\u6811\u7684\u6df1\u5ea6\u3002<\/li>\n<\/ul>\n\u6a21\u578b\u8bc4\u4f30\u4e0e\u89e3\u91ca<\/strong><\/p>\n\n\n\n| \u6307\u6807<\/th>\n | \u9002\u7528\u573a\u666f<\/th>\n<\/tr>\n<\/thead>\n | \n\n| Accuracy<\/td>\n | \u7c7b\u522b\u5747\u8861\u7684\u4e8c\u5206\u7c7b\u6216\u591a\u5206\u7c7b<\/td>\n<\/tr>\n | \n| F1\u2011Score<\/td>\n | \u7c7b\u522b\u4e0d\u5e73\u8861\u3001\u5173\u6ce8\u53ec\u56de\u7387<\/td>\n<\/tr>\n | \n| AUC\u2011ROC<\/td>\n | \u91d1\u878d\u98ce\u63a7\u3001\u6b3a\u8bc8\u68c0\u6d4b\u7b49\u9700\u8981\u6392\u5e8f\u80fd\u529b\u7684\u4efb\u52a1<\/td>\n<\/tr>\n | \n| \u7279\u5f81\u91cd\u8981\u6027\uff08Mean Decrease Impurity\uff09<\/td>\n | \u968f\u673a\u68ee\u6797\u3001XGBoost\uff0c\u53ef\u5e2e\u52a9\u4e1a\u52a1\u89e3\u91ca\u6a21\u578b<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n \u6a21\u578b\u89e3\u91ca\u5de5\u5177\u5982SHAP\u3001LIME \u80fd\u5bf9\u6bcf\u4e00\u6b21\u9884\u6d4b\u7ed9\u51fa\u5c40\u90e8\u89e3\u91ca\uff0c<\/strong>\u6ee1\u8db3\u76d1\u7ba1\u5bf9\u6a21\u578b\u900f\u660e\u5ea6\u7684\u8981\u6c42\u3002<\/p>\n\u524d\u6cbf\u8d8b\u52bf\u4e0e\u7814\u7a76\u70ed\u70b9<\/h2>\n\n- \u81ea\u52a8\u5316\u96c6\u6210\uff08AutoEnsemble\uff09<\/strong>\u200d\uff1a\u7ed3\u5408\u00a0AutoML\u00a0\u4e0e\u5143\u5b66\u4e60\uff0c\u81ea\u52a8\u641c\u7d22\u6700\u4f73\u57fa\u6a21\u578b\u7ec4\u5408\u3001\u7279\u5f81\u5b50\u7a7a\u95f4\u4e0e\u878d\u5408\u7b56\u7565\uff0c\u964d\u4f4e\u4eba\u5de5\u8c03\u53c2\u6210\u672c\u3002<\/li>\n
- \u8f7b\u91cf\u5316 Boosting<\/strong>\uff1aCatBoost\u3001LightGBM \u5728\u00a0GPU\u3001\u5206\u5e03\u5f0f \u73af\u5883\u4e0b\u7684\u52a0\u901f\u5b9e\u73b0\uff0c\u4f7f\u5f97 Boosting \u80fd\u5728\u8fb9\u7f18\u8bbe\u5907\uff08\u5982\u8f66\u8f7d\u7cfb\u7edf\u3001IoT\uff09\u4e0a\u5b9e\u73b0\u5b9e\u65f6\u63a8\u7406\u3002<\/li>\n
- \u6df1\u5ea6\u96c6\u6210<\/strong>\uff1a\u628a\u6df1\u5ea6\u795e\u7ecf\u7f51\u7edc\u4f5c\u4e3a\u57fa\u5b66\u4e60\u5668\uff0c\u914d\u5408 Stacking \u6216 Boosting\uff0c\u5df2\u5728\u591a\u6a21\u6001\u667a\u80fd\u4f53\u3001\u5927\u6a21\u578b\u5fae\u8c03\u4e2d\u5c55\u73b0\u51fa\u8de8\u6a21\u6001\u77e5\u8bc6\u8fc1\u79fb\u7684\u6f5c\u529b\u3002<\/li>\n
- \u89e3\u91ca\u6027\u589e\u5f3a<\/strong>\uff1a\u901a\u8fc7SHAP\u3001LIME \u4e0e\u7279\u5f81\u91cd\u8981\u6027\u53ef\u89c6\u5316\uff0c\u5b9e\u73b0\u5bf9\u6bcf\u4e00\u6b21\u9884\u6d4b\u7684\u53ef\u89e3\u91ca\uff0c\u6ee1\u8db3\u76d1\u7ba1\u5bf9\u6a21\u578b\u900f\u660e\u5ea6\u7684\u8981\u6c42\u3002<\/li>\n
- \u8de8\u6a21\u6001\u96c6\u6210<\/strong>\uff1a\u5728\u56fe\u50cf\u3001\u6587\u672c\u3001\u8bed\u97f3\u3001\u7ed3\u6784\u5316\u6570\u636e\u7b49\u591a\u6e90\u4fe1\u606f\u4e0a\u8fdb\u884c\u7edf\u4e00\u7684\u7279\u5f81\u878d\u5408\u4e0e\u6a21\u578b\u5806\u53e0\uff0c\u5df2\u5728\u667a\u80fd\u5ba2\u670d\u3001\u667a\u6167\u57ce\u5e02\u7b49\u573a\u666f\u53d6\u5f97\u7a81\u7834\u3002<\/li>\n<\/ul>\n
\u5e38\u89c1\u8bef\u533a\u4e0e\u6700\u4f73\u5b9e\u8df5<\/h2>\n\n\n\n| \u8bef\u533a<\/th>\n | \u6b63\u786e\u505a\u6cd5<\/th>\n<\/tr>\n<\/thead>\n | \n\n| \u53ea\u8ffd\u6c42\u66f4\u591a\u57fa\u6a21\u578b\u5ffd\u89c6\u591a\u6837\u6027<\/td>\n | \u786e\u4fdd\u57fa\u5b66\u4e60\u5668\u4e4b\u95f4\u8bef\u5dee\u4e0d\u5b8c\u5168\u76f8\u5173\uff08\u4e0d\u540c\u7b97\u6cd5\u3001\u4e0d\u540c\u7279\u5f81\u5b50\u7a7a\u95f4\uff09<\/td>\n<\/tr>\n | \n| \u5728 Boosting \u4e2d\u4f7f\u7528\u9ad8\u566a\u58f0\u6570\u636e<\/td>\n | \u5bf9\u5f02\u5e38\u6837\u672c\u8fdb\u884c\u9884\u5904\u7406\u6216\u91c7\u7528\u00a0Robust Boosting\uff08\u5982\u6b63\u5219\u5316\u3001\u5b50\u91c7\u6837\uff09<\/td>\n<\/tr>\n | \n| Stacking \u4e2d\u4fe1\u606f\u6cc4\u6f0f\uff08\u4f7f\u7528\u5b8c\u6574\u8bad\u7ec3\u96c6\u9884\u6d4b\uff09<\/td>\n | \u5fc5\u987b\u91c7\u7528\u4ea4\u53c9\u9a8c\u8bc1\u4ea7\u751f\u7b2c\u4e00\u5c42\u9884\u6d4b\uff0c\u9632\u6b62\u5143\u5b66\u4e60\u5668\u770b\u5230\u771f\u5b9e\u6807\u7b7e<\/td>\n<\/tr>\n | \n| \u968f\u673a\u68ee\u6797\u6811\u6df1\u5ea6\u65e0\u9650\u5236\u5bfc\u81f4\u8fc7\u62df\u5408<\/td>\n | \u901a\u8fc7\u00a0max_depth<\/code>\u3001min_samples_leaf<\/code><\/strong>\u00a0\u63a7\u5236\u6811\u7684\u590d\u6742\u5ea6<\/td>\n<\/tr>\n\n| \u53ea\u5173\u6ce8\u6a21\u578b\u7cbe\u5ea6\u5ffd\u89c6\u63a8\u7406\u6548\u7387<\/td>\n | \u5728\u90e8\u7f72\u524d\u8fdb\u884c\u6a21\u578b\u538b\u7f29\u3001\u84b8\u998f\u6216\u526a\u679d\uff0c\u786e\u4fdd\u6ee1\u8db3\u5b9e\u65f6\u4e1a\u52a1\u9700\u6c42<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\u603b\u7ed3<\/h2>\n\u7efc\u4e0a\u6240\u8ff0\uff0c\u96c6\u6210\u5b66\u4e60\u901a\u8fc7\u6a21\u578b\u534f\u540c\u8fd9\u4e00\u6838\u5fc3\u539f\u5219\uff0c\u7cfb\u7edf\u6027\u5730\u89e3\u51b3\u4e86\u5355\u4e00\u6a21\u578b\u9762\u4e34\u7684\u504f\u5dee-\u65b9\u5dee\u56f0\u5883\uff0c\u6210\u4e3a\u63d0\u5347\u673a\u5668\u5b66\u4e60\u6027\u80fd\u7684\u5178\u8303\u6027\u7b56\u7565\u3002\u4ece\u65b9\u6cd5\u8bba\u4e0a\uff0cBagging\u3001Boosting\u3001Stacking\u3001\u968f\u673a\u68ee\u6797\u548c\u6295\u7968\u6cd5\u6784\u6210\u4e86\u4e00\u4e2a\u5c42\u6b21\u4e30\u5bcc\u3001\u5404\u6709\u4fa7\u91cd\u7684\u6280\u672f\u4f53\u7cfb\uff0c\u4f7f\u5b9e\u8df5\u8005\u80fd\u6839\u636e\u6570\u636e\u7279\u6027\u4e0e\u4e1a\u52a1\u76ee\u6807\u7cbe\u51c6\u5339\u914d\u65b9\u6848\u3002\u5728\u5de5\u7a0b\u5b9e\u8df5\u4e2d\uff0c\u5df2\u5f62\u6210\u4ece\u6570\u636e\u51c6\u5907\u5230\u90e8\u7f72\u76d1\u63a7\u7684\u6807\u51c6\u5316\u6d41\u7a0b\uff0c\u517c\u5177\u5f3a\u5927\u7684\u6548\u80fd\u4e0e\u53ef\u843d\u5730\u6027\u3002\u5c55\u671b\u672a\u6765\uff0c\u968f\u7740AutoML\u3001\u53ef\u89e3\u91ca\u6027AI\u7b49\u65b9\u5411\u7684\u6df1\u5ea6\u878d\u5408\u53d1\u5c55\uff0c\u96c6\u6210\u5b66\u4e60\u5c06\u7ee7\u7eed\u4f5c\u4e3a\u63a8\u52a8\u4eba\u5de5\u667a\u80fd\u5728\u590d\u6742\u573a\u666f\u4e2d\u5b9e\u73b0\u66f4\u53ef\u9760\u3001\u66f4\u9ad8\u6548\u3001\u66f4\u900f\u660e\u51b3\u7b56\u7684\u5173\u952e\u9a71\u52a8\u529b\u3002<\/p>\n","protected":false},"excerpt":{"rendered":" \u5728\u5f53\u524d\u673a\u5668\u5b66\u4e60\u9762\u4e34\u590d\u6742\u4efb\u52a1\u4e0e\u9ad8\u6cdb\u5316\u8981\u6c42\u7684\u80cc\u666f\u4e0b\uff0c\u96c6\u6210\u5b66\u4e60\uff08Ensemble Learning\uff09\u4f5c\u4e3a\u901a\u8fc7\u6784\u5efa\u3001\u7ec4\u5408\u591a\u4e2a\u57fa\u5b66\u4e60\u5668\u7a81\u7834\u5355\u4e00\u6a21\u578b\u6027\u80fd\u74f6\u9888\u7684\u5148\u8fdb\u8303\u5f0f\uff0c\u51ed\u501f\u624e\u5b9e\u7684\u7406\u8bba\u57fa\u7840\u4e0e\u5353\u8d8a\u7684\u5b9e\u8df5\u6548\u80fd\uff0c\u5df2\u6210\u4e3a\u63d0\u5347\u6a21\u578b\u9c81\u68d2\u6027\u4e0e\u9884\u6d4b\u51c6\u786e\u6027\u7684\u6838\u5fc3\u624b\u6bb5\u3002\u672c\u62a5\u544a\u7cfb\u7edf\u9610\u8ff0\u4e86\u4ece\u201c\u96c6\u4f53\u667a\u6167\u201d\u6838\u5fc3\u7406\u5ff5\uff0c\u5230Bagging\u3001Boosting\u7b49\u4e3b\u6d41\u65b9\u6cd5\uff0c\u76f4\u81f3\u5b9e\u9645\u90e8\u7f72\u4e0e\u524d\u6cbf\u8d8b\u52bf\u7684\u5b8c\u6574\u77e5\u8bc6\u4f53\u7cfb\uff0c\u4e3a\u8bfb\u8005\u63d0\u4f9b\u4e00\u5957\u7406\u89e3\u4e0e\u5e94\u7528\u8fd9\u4e00\u5f3a\u5927\u5de5\u5177\u7684\u7cfb\u7edf\u6846\u67b6\u3002 \u4ec0\u4e48\u662f\u96c6\u6210\u5b66\u4e60\uff1f \u96c6\u6210\u5b66\u4e60\u662f\u673a\u5668\u5b66\u4e60\u8303\u5f0f\uff0c\u6838\u5fc3\u601d\u60f3\u662f\u8ba9\u591a\u4e2a\u6a21\u578b\u4e00\u8d77\u5de5\u4f5c\uff0c\u901a\u8fc7\u5bf9\u9884\u6d4b\u7ed3\u679c\u8fdb\u884c\u5408\u7406\u7684\u7ec4\u5408\uff0c\u5f97\u5230\u6bd4\u4efb\u4f55\u5355\u4e2a\u6a21\u578b\u90fd\u66f4\u7a33\u5065\u3001\u66f4\u51c6\u786e\u7684\u6700\u7ec8\u9884\u6d4b\u3002\u53ef\u4ee5\u628a\u5b83\u60f3\u8c61\u6210\u4e00\u6b21\u201c\u56e2\u961f\u51b3\u7b56\u201d\uff1a\u6bcf\u4e2a\u57fa\u5b66\u4e60\u5668\uff08\u4e5f\u53eb\u5b50\u6a21\u578b\uff09\u90fd\u6709\u81ea\u5df1\u7684\u201c\u4e13\u957f\u201d\u548c\u201c\u76f2\u70b9\u201d\uff0c\u5f53\u5b83\u4eec\u4e00\u8d77\u8ba8\u8bba\u3001\u6295\u7968\u6216\u76f8\u4e92\u7ea0\u9519\u65f6\uff0c\u6574\u4f53\u7684\u5224\u65ad\u4f1a\u66f4\u52a0\u53ef\u9760\u3002 \u4ece\u6280\u672f\u5c42\u9762\u770b\uff0c\u96c6\u6210\u5b66\u4e60\u7684\u5b9e\u73b0\u65b9\u5f0f\u4e3b\u8981\u5305\u62ec\u5e76\u884c\uff08\u5982 Bagging\u3001\u968f\u673a\u68ee\u6797\uff09\u548c\u987a\u5e8f\uff08\u5982 Boosting\uff09\u4e24\u5927\u7c7b\u3002\u5e76\u884c\u65b9\u5f0f\u5f3a\u8c03\u591a\u6837\u6027\uff0c\u5728\u4e0d\u540c\u7684\u8bad\u7ec3\u5b50\u96c6\u6216\u7279\u5f81\u5b50\u7a7a\u95f4\u4e0a\u8bad\u7ec3\u591a\u4e2a\u6a21\u578b\uff0c\u4f7f\u5f97\u9519\u8bef\u4e0d\u5b8c\u5168\u76f8\u5173\uff1b\u987a\u5e8f\u65b9\u5f0f\u5f3a\u8c03\u9010\u6b65\u7ea0\u9519\uff0c\u6bcf\u4e00\u6b65\u90fd\u5173\u6ce8\u524d\u4e00\u6b65\u7684\u9519\u8bef\uff0c\u901a\u8fc7\u52a0\u6743\u6216\u68af\u5ea6\u4e0b\u964d\u7684\u65b9\u5f0f\u628a\u5f31\u6a21\u578b\u9010\u6e10\u63d0\u5347\u4e3a\u5f3a\u6a21\u578b\u3002 \u201c\u96c6\u6210\u5b66\u4e60\uff08Ensemble Learning\uff09\u662f\u4e00\u79cd\u673a\u5668\u5b66\u4e60\u8303\u5f0f\uff0c\u901a\u8fc7\u8bad\u7ec3\u591a\u4e2a\u5b66\u4e60\u5668\u89e3\u51b3\u540c\u4e00\u4e2a\u95ee\u9898\u3002\u201d \u96c6\u6210\u5b66\u4e60\u7684\u57fa\u672c\u539f\u7406 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