{"id":90842,"date":"2026-08-06T06:48:45","date_gmt":"2026-08-05T22:48:45","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/90842.html"},"modified":"2026-08-06T06:48:45","modified_gmt":"2026-08-05T22:48:45","slug":"%e4%ba%ba%e5%b7%a5%e6%99%ba%e8%83%bdscikit-learn%ef%bc%9apython%e4%b8%ad%e7%9a%84%e5%ae%9e%e7%94%a8%e6%9c%ba%e5%99%a8%e5%ad%a6%e4%b9%a0%e5%ba%93","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/90842.html","title":{"rendered":"[\u4eba\u5de5\u667a\u80fd]Scikit-learn\uff1aPython\u4e2d\u7684\u5b9e\u7528\u673a\u5668\u5b66\u4e60\u5e93"},"content":{"rendered":"

Scikit-learn：Python\u4e2d\u7684\u5b9e\u7528\u673a\u5668\u5b66\u4e60\u5e93<\/span><\/p>\n

\u672c\u6587\u9762\u5411\u5de5\u7a0b\u5b9e\u8df5\u4ecb\u7ecdPython\u673a\u5668\u5b66\u4e60\u5e93scikit-learn，\u5305\u62ec\u5176\u8bbe\u8ba1\u7406\u5ff5\u3001\u6838\u5fc3API\u7ea6\u5b9a\u4ee5\u53ca\u4e3b\u8981\u4f30\u8ba1\u5668\u5bb6\u65cf\u3002\u540c\u65f6\u8ba8\u8bba\u6570\u636e\u9884\u5904\u7406\u3001\u6a21\u578b\u9009\u62e9\u548c\u8bc4\u4f30\u7b49\u5178\u578b\u5de5\u4f5c\u6d41\u7a0b，\u5e76\u901a\u8fc7\u793a\u610f\u56fe\u8868\u8bf4\u660e\u5e38\u89c1\u7528\u6cd5\u3002\u6587\u4e2d\u793a\u4f8b\u4ec5\u4e3a\u793a\u610f，\u4e0d\u4ee3\u8868\u5177\u4f53\u4ea7\u54c1\u6216\u6807\u51c6\u6027\u80fd\u6307\u6807\u3002<\/p>\n

\"\"<\/p>\n

\u56fe1：\u82e5\u5e72scikit-learn\u5206\u7c7b\u5668\u5728\u793a\u610f\u6570\u636e\u96c6\u4e0a\u7684Accuracy\u5bf9\u6bd4（\u793a\u610f）\u3002<\/p>\n

\"\"<\/p>\n

\u56fe2：\u793a\u610f\u8bad\u7ec3\u6837\u672c\u6570\u91cf\u4e0e\u4ea4\u53c9\u9a8c\u8bc1\u5f97\u5206\u4e4b\u95f4\u7684\u5b66\u4e60\u66f2\u7ebf\u5173\u7cfb（\u793a\u610f）\u3002<\/p>\n

\"\"<\/p>\n

\u56fe3：\u4f7f\u7528scikit-learn\u6784\u5efa\u591a\u5206\u7c7b\u6a21\u578b\u5f97\u5230\u7684\u6df7\u6dc6\u77e9\u9635\u793a\u610f\u3002<\/p>\n\n\n\n\n\n\n\n\n
\n

\u7b97\u6cd5\u7c7b\u522b<\/span><\/p>\n<\/td>\n

\n

\u793a\u4f8b\u4f30\u8ba1\u5668<\/span><\/p>\n<\/td>\n

\n

\u5178\u578b\u7528\u9014<\/span><\/p>\n<\/td>\n

\n

\u8bf4\u660e<\/span><\/p>\n<\/td>\n<\/tr>\n

\n

\u7ebf\u6027\u6a21\u578b<\/p>\n<\/td>\n

\n

LogisticRegression<\/p>\n<\/td>\n

\n

\u4e8c\u5206\u7c7b\/\u591a\u5206\u7c7b\u4efb\u52a1\u3002<\/p>\n<\/td>\n

\n

\u7cfb\u6570\u53ef\u89e3\u91ca，\u652f\u6301L1\/L2\u6b63\u5219\u5316\u3002<\/p>\n<\/td>\n<\/tr>\n

\n

\u6811\u6a21\u578b<\/p>\n<\/td>\n

\n

RandomForestClassifier<\/p>\n<\/td>\n

\n

\u9c81\u68d2\u5206\u7c7b\u53ca\u7279\u5f81\u91cd\u8981\u6027\u5206\u6790\u3002<\/p>\n<\/td>\n

\n

\u9002\u5408\u8868\u683c\u6570\u636e，\u80fd\u5904\u7406\u6df7\u5408\u7c7b\u578b\u7279\u5f81\u3002<\/p>\n<\/td>\n<\/tr>\n

\n

\u652f\u6301\u5411\u91cf\u673a<\/p>\n<\/td>\n

\n

SVC<\/p>\n<\/td>\n

\n

\u9ad8\u7ef4\u7a7a\u95f4\u5206\u7c7b\u3002<\/p>\n<\/td>\n

\n

\u901a\u8fc7\u6838\u6280\u5de7\u5b9e\u73b0\u975e\u7ebf\u6027\u51b3\u7b56\u8fb9\u754c\u3002<\/p>\n<\/td>\n<\/tr>\n

\n

\u805a\u7c7b<\/p>\n<\/td>\n

\n

KMeans<\/p>\n<\/td>\n

\n

\u65e0\u76d1\u7763\u6837\u672c\u5206\u7ec4\u3002<\/p>\n<\/td>\n

\n

\u5bf9\u521d\u59cb\u5316\u548c\u7279\u5f81\u7f29\u653e\u8f83\u654f\u611f\u3002<\/p>\n<\/td>\n<\/tr>\n

\n

\u964d\u7ef4<\/p>\n<\/td>\n

\n

PCA<\/p>\n<\/td>\n

\n

\u7279\u5f81\u538b\u7f29\u53ca\u53ef\u89c6\u5316\u3002<\/p>\n<\/td>\n

\n

\u65e0\u76d1\u7763\u65b9\u6cd5，\u6355\u6349\u65b9\u5dee\u4e3b\u65b9\u5411\u3002<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\u88681：\u5e38\u89c1scikit-learn\u4f30\u8ba1\u5668\u7c7b\u522b\u53ca\u793a\u4f8b\u7c7b\u3002<\/p>\n\n\n\n\n\n\n\n
\n

\u51fd\u6570<\/span><\/p>\n<\/td>\n

\n

\u7528\u9014<\/span><\/p>\n<\/td>\n

\n

\u5173\u952e\u53c2\u6570<\/span><\/p>\n<\/td>\n

\n

\u8bf4\u660e<\/span><\/p>\n<\/td>\n<\/tr>\n

\n

train_test_split<\/p>\n<\/td>\n

\n

\u5c06\u6570\u7ec4\u62c6\u5206\u4e3a\u8bad\u7ec3\/\u6d4b\u8bd5\u96c6\u3002<\/p>\n<\/td>\n

\n

test_size, random_state, stratify<\/p>\n<\/td>\n

\n

\u5e38\u7528\u4e8e\u521d\u6b65\u5b9e\u9a8c\u3002<\/p>\n<\/td>\n<\/tr>\n

\n

KFold<\/p>\n<\/td>\n

\n

K\u6298\u4ea4\u53c9\u9a8c\u8bc1\u8fed\u4ee3\u5668\u3002<\/p>\n<\/td>\n

\n

n_splits, shuffle<\/p>\n<\/td>\n

\n

\u9ed8\u8ba4\u4e0d\u4fdd\u6301\u7c7b\u522b\u6bd4\u4f8b\u3002<\/p>\n<\/td>\n<\/tr>\n

\n

StratifiedKFold<\/p>\n<\/td>\n

\n

\u4fdd\u6301\u7c7b\u522b\u6bd4\u4f8b\u7684K\u6298\u4ea4\u53c9\u9a8c\u8bc1\u3002<\/p>\n<\/td>\n

\n

n_splits, shuffle<\/p>\n<\/td>\n

\n

\u63a8\u8350\u7528\u4e8e\u5206\u7c7b\u4efb\u52a1\u3002<\/p>\n<\/td>\n<\/tr>\n

\n

GroupKFold<\/p>\n<\/td>\n

\n

\u6309\u7ec4\u5212\u5206\u7684\u4ea4\u53c9\u9a8c\u8bc1\u3002<\/p>\n<\/td>\n

\n

n_splits<\/p>\n<\/td>\n

\n

\u9002\u7528\u4e8e\u5b58\u5728\u7528\u6237\/\u4f1a\u8bdd\u7b49\u7ec4\u7684\u573a\u666f，\u907f\u514d\u6570\u636e\u6cc4\u6f0f\u3002<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\u88682：scikit-learn\u4e2d\u5e38\u7528\u7684\u6570\u636e\u5212\u5206\u548c\u4ea4\u53c9\u9a8c\u8bc1\u5de5\u5177\u3002<\/p>\n\n\n\n\n\n\n\n\n
\n

\u6307\u6807<\/span><\/p>\n<\/td>\n

\n

\u4efb\u52a1\u7c7b\u578b<\/span><\/p>\n<\/td>\n

\n

\u51fd\u6570<\/span><\/p>\n<\/td>\n

\n

\u8bf4\u660e<\/span><\/p>\n<\/td>\n<\/tr>\n

\n

Accuracy<\/p>\n<\/td>\n

\n

\u5206\u7c7b<\/p>\n<\/td>\n

\n

accuracy_score<\/p>\n<\/td>\n

\n

\u5bf9\u7c7b\u522b\u4e0d\u5e73\u8861\u654f\u611f\u3002<\/p>\n<\/td>\n<\/tr>\n

\n

Precision\/Recall\/F1<\/p>\n<\/td>\n

\n

\u5206\u7c7b<\/p>\n<\/td>\n

\n

precision_score, recall_score, f1_score<\/p>\n<\/td>\n

\n

\u5173\u6ce8\u6b63\u7c7b\u6027\u80fd，\u53ef\u901a\u8fc7\u52a0\u6743\u7248\u672c\u7efc\u5408\u8bc4\u4f30\u3002<\/p>\n<\/td>\n<\/tr>\n

\n

ROC AUC<\/p>\n<\/td>\n

\n

\u4e8c\u5206\u7c7b<\/p>\n<\/td>\n

\n

roc_auc_score<\/p>\n<\/td>\n

\n

\u8bc4\u4f30\u9884\u6d4b\u5f97\u5206\u6392\u5e8f\u8d28\u91cf\u3002<\/p>\n<\/td>\n<\/tr>\n

\n

MSE\/RMSE<\/p>\n<\/td>\n

\n

\u56de\u5f52<\/p>\n<\/td>\n

\n

mean_squared_error<\/p>\n<\/td>\n

\n

\u5bf9\u5927\u8bef\u5dee\u60e9\u7f5a\u66f4\u5f3a\u3002<\/p>\n<\/td>\n<\/tr>\n

\n

R^2<\/p>\n<\/td>\n

\n

\u56de\u5f52<\/p>\n<\/td>\n

\n

r2_score<\/p>\n<\/td>\n

\n

\u89e3\u91ca\u65b9\u5dee\u6bd4\u4f8b，\u53ef\u80fd\u4e3a\u8d1f\u503c\u3002<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\u88683：\u5e38\u89c1\u8bc4\u4f30\u6307\u6807\u53ca\u5176\u5728scikit-learn\u4e2d\u7684\u51fd\u6570\u5b9e\u73b0\u3002<\/p>\n

1. scikit-learn\u7684\u8bbe\u8ba1\u7406\u5ff5<\/span><\/h2>\n

scikit-learn\u91c7\u7528\u7edf\u4e00\u7684\u9762\u5411\u5bf9\u8c61API\u8bbe\u8ba1，\u56f4\u7ed5\u4f30\u8ba1\u5668\u5bf9\u8c61\u5c55\u5f00\u3002\u4f30\u8ba1\u5668\u901a\u5e38\u5b9e\u73b0fit\u548cpredict\u65b9\u6cd5（\u5bf9\u4e8e\u8f6c\u6362\u5668\u5219\u5b9e\u73b0transform），\u5e76\u901a\u8fc7\u4ee5\u4e0b\u5212\u7ebf\u7ed3\u5c3e\u7684\u5c5e\u6027\u66b4\u9732\u5b66\u4e60\u5230\u7684\u53c2\u6570\u3002\u8fd9\u79cd\u4e00\u81f4\u63a5\u53e3\u4f7f\u5f97\u5728\u4e0d\u540c\u6a21\u578b\u4e4b\u95f4\u5207\u6362\u3001\u6784\u5efa\u6d41\u6c34\u7ebf\u4ee5\u53ca\u4e0eNumPy\u3001SciPy\u3001pandas\u7b49\u79d1\u5b66\u8ba1\u7b97\u5e93\u96c6\u6210\u53d8\u5f97\u5341\u5206\u65b9\u4fbf\u3002<\/p>\n

\u53e6\u4e00\u4e2a\u91cd\u8981\u8bbe\u8ba1\u9009\u62e9\u662f\u805a\u7126\u4e8e\u7ecf\u5178\u673a\u5668\u5b66\u4e60\u65b9\u6cd5，\u800c\u975e\u6df1\u5ea6\u5b66\u4e60\u3002\u4f8b\u5982\u7ebf\u6027\u6a21\u578b\u3001\u6811\u6a21\u578b\u3001\u805a\u7c7b\u4e0e\u7279\u5f81\u9009\u62e9\u7b49\u3002\u8fd9\u4f7f\u5f97scikit-learn\u975e\u5e38\u9002\u5408\u5904\u7406\u8868\u683c\u6570\u636e\u548c\u4f20\u7edf\u4e1a\u52a1\u95ee\u9898，\u5e76\u652f\u6301\u5feb\u901f\u539f\u578b\u5f00\u53d1\u3002\u90e8\u5206\u4f30\u8ba1\u5668\u901a\u8fc7n_jobs\u7b49\u53c2\u6570\u652f\u6301\u591a\u6838\u5e76\u884c，\u4fbf\u4e8e\u5728\u4e2d\u7b49\u89c4\u6a21\u6570\u636e\u96c6\u4e0a\u63d0\u5347\u8ba1\u7b97\u6548\u7387\u3002<\/p>\n

2. \u6570\u636e\u8868\u793a\u4e0e\u9884\u5904\u7406<\/span><\/h2>\n

scikit-learn\u4e3b\u8981\u64cd\u4f5cNumPy\u6570\u7ec4\u548cSciPy\u7a00\u758f\u77e9\u9635，\u7ea6\u5b9a\u6837\u672c\u4e3a\u884c\u3001\u7279\u5f81\u4e3a\u5217\u3002\u5e93\u4e2d\u63d0\u4f9b\u591a\u79cd\u9884\u5904\u7406\u8f6c\u6362\u5668\u7528\u4e8e\u5904\u7406\u7f3a\u5931\u503c\u3001\u8fdb\u884c\u7279\u5f81\u7f29\u653e\u3001\u7f16\u7801\u7c7b\u522b\u7279\u5f81\u4ee5\u53ca\u751f\u6210\u591a\u9879\u5f0f\u7279\u5f81\u3002\u5e38\u89c1\u7684\u8f6c\u6362\u5668\u5305\u62ecStandardScaler\u3001MinMaxScaler\u3001OneHotEncoder\u3001SimpleImputer\u7b49\u3002<\/p>\n

\u8f6c\u6362\u5668\u9075\u5faa\u4e0e\u4f30\u8ba1\u5668\u7c7b\u4f3c\u7684API：\u5728\u8bad\u7ec3\u6570\u636e\u4e0a\u8c03\u7528fit\u8fdb\u884c\u62df\u5408，\u7136\u540e\u901a\u8fc7transform（\u6216fit_transform）\u5bf9\u6570\u636e\u8fdb\u884c\u8f6c\u6362\u3002\u591a\u6b65\u9884\u5904\u7406\u4e0e\u6700\u7ec8\u6a21\u578b\u901a\u5e38\u901a\u8fc7Pipeline\u6216ColumnTransformer\u7ec4\u5408\u4e3a\u5355\u4e00\u590d\u5408\u4f30\u8ba1\u5668，\u4ece\u800c\u5728\u4ea4\u53c9\u9a8c\u8bc1\u548c\u90e8\u7f72\u9636\u6bb5\u7edf\u4e00\u5904\u7406\u3002\u8fd9\u6709\u52a9\u4e8e\u907f\u514d\u6570\u636e\u6cc4\u6f0f，\u5e76\u4fdd\u8bc1\u8bad\u7ec3\u4e0e\u9884\u6d4b\u9636\u6bb5\u7684\u7279\u5f81\u5904\u7406\u903b\u8f91\u4e00\u81f4\u3002<\/p>\n

3. \u5173\u952e\u4f30\u8ba1\u5668\u5bb6\u65cf<\/span><\/h2>\n

\u5728\u76d1\u7763\u5b66\u4e60\u65b9\u9762，scikit-learn\u63d0\u4f9b\u4e86\u7ebf\u6027\u56de\u5f52\u4e0e\u5206\u7c7b（LinearRegression, LogisticRegression）\u3001\u6811\u6a21\u578b（DecisionTree\u3001RandomForest\u3001GradientBoosting）\u3001\u652f\u6301\u5411\u91cf\u673a（SVC, SVR）\u3001\u8fd1\u90bb\u7b97\u6cd5（KNeighborsClassifier, KNeighborsRegressor）\u3001\u6734\u7d20\u8d1d\u53f6\u65af\u7b49\u3002\u6bcf\u4e2a\u4f30\u8ba1\u5668\u90fd\u63d0\u4f9b\u82e5\u5e72\u8d85\u53c2\u6570，\u7528\u4e8e\u63a7\u5236\u6b63\u5219\u5316\u5f3a\u5ea6\u3001\u6811\u6df1\u5ea6\u3001\u6838\u51fd\u6570\u7c7b\u578b\u3001\u8fd1\u90bb\u6570\u91cf\u7b49\u3002<\/p>\n

\u5728\u975e\u76d1\u7763\u5b66\u4e60\u65b9\u9762，\u5e93\u4e2d\u5305\u542bKMeans\u3001AgglomerativeClustering\u3001DBSCAN\u7b49\u805a\u7c7b\u7b97\u6cd5，\u4ee5\u53caPCA\u3001TruncatedSVD\u53ca\u591a\u79cd\u6d41\u5f62\u5b66\u4e60\u65b9\u6cd5\u7b49\u964d\u7ef4\u6280\u672f\u3002\u8fd9\u4e9b\u5de5\u5177\u5e38\u7528\u4e8e\u63a2\u7d22\u6027\u6570\u636e\u5206\u6790\u3001\u53ef\u89c6\u5316\u548c\u7279\u5f81\u5de5\u7a0b，\u4e3a\u540e\u7eed\u76d1\u7763\u5b66\u4e60\u6a21\u578b\u63d0\u4f9b\u66f4\u597d\u7684\u8f93\u5165\u8868\u793a\u3002<\/p>\n

4. \u6a21\u578b\u9009\u62e9\u4e0e\u8d85\u53c2\u6570\u8c03\u4f18<\/span><\/h2>\n

\u5728\u5b9e\u9645\u4f7f\u7528scikit-learn\u65f6，\u6a21\u578b\u9009\u62e9\u548c\u8d85\u53c2\u6570\u8c03\u4f18\u662f\u6838\u5fc3\u6b65\u9aa4\u4e4b\u4e00\u3002\u5e93\u4e2d\u63d0\u4f9bGridSearchCV\u548cRandomizedSearchCV\u7b49\u5de5\u5177\u5305\u88c5\u4f30\u8ba1\u5668，\u5728\u7ed9\u5b9a\u8d85\u53c2\u6570\u7a7a\u95f4\u4e0a\u901a\u8fc7\u4ea4\u53c9\u9a8c\u8bc1\u641c\u7d22\u6027\u80fd\u66f4\u4f18\u7684\u914d\u7f6e\u3002\u641c\u7d22\u7ed3\u679c\u672c\u8eab\u662f\u4e00\u4e2a\u65b0\u7684\u4f30\u8ba1\u5668\u5bf9\u8c61，\u66b4\u9732\u6700\u4f73\u53c2\u6570\u5e76\u53ef\u50cf\u666e\u901a\u6a21\u578b\u4e00\u6837\u4f7f\u7528\u3002<\/p>\n

\u5728\u66f4\u590d\u6742\u573a\u666f\u4e2d，\u53ef\u4ee5\u7ed3\u5408\u9010\u6b21\u6dd8\u6c70（successive halving）\u6216\u5916\u90e8\u5e93\u63d0\u4f9b\u7684\u8d1d\u53f6\u65af\u4f18\u5316\u65b9\u6cd5\u6765\u63d0\u5347\u8c03\u4f18\u6548\u7387\u3002\u5408\u7406\u9009\u62e9\u641c\u7d22\u7a7a\u95f4\u3001\u8bc4\u5206\u51fd\u6570\u4ee5\u53ca\u4ea4\u53c9\u9a8c\u8bc1\u7b56\u7565，\u5bf9\u4e8e\u907f\u514d\u5728\u9a8c\u8bc1\u96c6\u4e0a\u8fc7\u62df\u5408\u3001\u63d0\u9ad8\u6a21\u578b\u6cdb\u5316\u80fd\u529b\u81f3\u5173\u91cd\u8981\u3002<\/p>\n

5. \u6a21\u578b\u8bc4\u4f30\u4e0e\u8bca\u65ad<\/span><\/h2>\n

scikit-learn\u5185\u7f6e\u591a\u79cd\u8bc4\u4f30\u6307\u6807\u548c\u8f85\u52a9\u51fd\u6570\u3002\u5bf9\u4e8e\u5206\u7c7b\u4efb\u52a1，\u5e38\u7528\u6307\u6807\u6709accuracy\u3001precision\u3001recall\u3001F1\u4ee5\u53caROC AUC；\u5bf9\u4e8e\u56de\u5f52\u4efb\u52a1，\u5219\u5e38\u7528MSE\u3001MAE\u548cR^2\u7b49\u3002\u6df7\u6dc6\u77e9\u9635\u3001\u5206\u7c7b\u62a5\u544a\u548cROC\u66f2\u7ebf\u53ef\u4ee5\u63d0\u4f9b\u6bd4\u5355\u4e2a\u6807\u91cf\u6307\u6807\u66f4\u4e30\u5bcc\u7684\u8bca\u65ad\u4fe1\u606f\u3002<\/p>\n

\u6a21\u578b\u8bc4\u4f30\u901a\u5e38\u901a\u8fc7cross_val_score\u6216cross_validate\u5b9e\u73b0，\u8fd9\u4e9b\u51fd\u6570\u63a5\u6536\u4f30\u8ba1\u5668\u3001\u6570\u636e\u548c\u8bc4\u5206\u51fd\u6570，\u5e76\u8fd4\u56de\u5404\u6298\u4e0a\u7684\u5f97\u5206\u3002\u6b64\u5916\u8fd8\u53ef\u4ee5\u7ed8\u5236\u5b66\u4e60\u66f2\u7ebf\u548c\u9a8c\u8bc1\u66f2\u7ebf，\u4ee5\u5206\u6790\u6027\u80fd\u968f\u8bad\u7ec3\u96c6\u5927\u5c0f\u6216\u67d0\u4e2a\u8d85\u53c2\u6570\u53d8\u5316\u7684\u8d8b\u52bf，\u5e2e\u52a9\u8bc6\u522b\u6b20\u62df\u5408\u3001\u8fc7\u62df\u5408\u4ee5\u53ca\u6570\u636e\u4e0d\u8db3\u7b49\u95ee\u9898\u3002<\/p>\n

6. \u6d41\u6c34\u7ebf\u4e0e\u7279\u5f81\u5de5\u7a0b<\/span><\/h2>\n

Pipeline\u662fscikit-learn\u4e2d\u975e\u5e38\u91cd\u8981\u7684\u6982\u5ff5，\u5c06\u591a\u4e2a\u6b65\u9aa4（\u901a\u5e38\u662f\u9884\u5904\u7406\u548c\u6700\u7ec8\u6a21\u578b）\u4e32\u8054\u4e3a\u4e00\u4e2a\u6574\u4f53\u5bf9\u8c61\u3002\u8fd9\u786e\u4fdd\u8bad\u7ec3\u9636\u6bb5\u7684\u6240\u6709\u8f6c\u6362\u5728\u8bc4\u4f30\u548c\u90e8\u7f72\u9636\u6bb5\u540c\u6837\u6267\u884c，\u51cf\u5c11\u7279\u5f81\u5904\u7406\u4e0d\u4e00\u81f4\u6216\u6570\u636e\u6cc4\u6f0f\u7684\u98ce\u9669\u3002<\/p>\n

\u7279\u5f81\u5de5\u7a0b\u5e38\u6d89\u53ca\u7ec4\u5408\u539f\u59cb\u7279\u5f81\u3001\u6784\u9020\u4ea4\u4e92\u9879\u6216\u6309\u65f6\u95f4\u7a97\u53e3\u805a\u5408\u4fe1\u606f\u3002scikit-learn\u63d0\u4f9bFunctionTransformer\u4ee5\u53ca\u81ea\u5b9a\u4e49\u8f6c\u6362\u5668\u63a5\u53e3，\u4fbf\u4e8e\u5c06\u9886\u57df\u7279\u5b9a\u7684\u7279\u5f81\u5904\u7406\u903b\u8f91\u7eb3\u5165\u7edf\u4e00\u7684\u6d41\u6c34\u7ebfAPI\u4e2d，\u4ece\u800c\u63d0\u9ad8\u4ee3\u7801\u6a21\u5757\u5316\u7a0b\u5ea6\u5e76\u5728\u591a\u9879\u76ee\u4e4b\u95f4\u590d\u7528\u3002<\/p>\n

7. \u5904\u7406\u7c7b\u522b\u4e0d\u5e73\u8861\u53ca\u5176\u4ed6\u5b9e\u8df5\u95ee\u9898<\/span><\/h2>\n

\u8bb8\u591a\u771f\u5b9e\u5206\u7c7b\u4efb\u52a1\u5b58\u5728\u7c7b\u522b\u4e0d\u5e73\u8861\u95ee\u9898\u3002\u4f7f\u7528scikit-learn\u65f6，\u5e38\u89c1\u7b56\u7565\u5305\u62ec\u5728\u4f30\u8ba1\u5668\u4e0a\u8bbe\u7f6eclass_weight\u3001\u9009\u62e9\u5173\u6ce8\u5c11\u6570\u7c7b\u8868\u73b0\u7684\u8bc4\u4f30\u6307\u6807，\u4ee5\u53ca\u501f\u52a9imbalanced-learn\u7b49\u5916\u90e8\u5e93\u8fdb\u884c\u8fc7\u91c7\u6837\u6216\u6b20\u91c7\u6837\u5904\u7406\u3002\u5408\u7406\u5229\u7528StratifiedKFold\u548c\u5206\u5c42\u8bad\u7ec3\/\u6d4b\u8bd5\u5212\u5206\u5bf9\u4e8e\u4fdd\u6301\u7c7b\u522b\u6bd4\u4f8b\u540c\u6837\u91cd\u8981\u3002<\/p>\n

\u5176\u4ed6\u5b9e\u8df5\u95ee\u9898\u5305\u62ec\u7279\u5f81\u7f29\u653e\u4e0e\u5f52\u4e00\u5316\u3001\u7f3a\u5931\u503c\u5904\u7406\u3001\u9ad8\u57fa\u6570\u7c7b\u522b\u7279\u5f81\u7f16\u7801\u4ee5\u53ca\u901a\u8fc7random_state\u63a7\u5236\u53ef\u91cd\u590d\u6027\u7b49\u3002\u6a21\u578b\u65e5\u5fd7\u8bb0\u5f55\u3001\u7248\u672c\u7ba1\u7406\u548c\u6d41\u6c34\u7ebf\u6253\u5305\u90e8\u7f72\u901a\u5e38\u5728\u5916\u56f4\u5e94\u7528\u4ee3\u7801\u4e2d\u5b9e\u73b0，\u4f46\u4e5f\u53d7\u76ca\u4e8escikit-learn\u7edf\u4e00\u7b80\u6d01\u7684API\u8bbe\u8ba1\u3002<\/p>\n

8. \u5728\u751f\u4ea7\u73af\u5883\u4e2d\u96c6\u6210scikit-learn<\/span><\/h2>\n

\u867d\u7136scikit-learn\u4e3b\u8981\u9762\u5411\u7814\u7a76\u548c\u539f\u578b\u5f00\u53d1，\u4f46\u5f88\u591a\u56e2\u961f\u4e5f\u5728\u751f\u4ea7\u7cfb\u7edf\u4e2d\u4f7f\u7528\u5b83\u3002\u6a21\u578b\u548c\u6d41\u6c34\u7ebf\u53ef\u4ee5\u901a\u8fc7joblib\u6216pickle\u8fdb\u884c\u5e8f\u5217\u5316，\u7531Web\u670d\u52a1\u3001\u6279\u5904\u7406\u6216\u6d41\u5f0f\u5e94\u7528\u52a0\u8f7d\u548c\u8c03\u7528\u3002\u5728\u6b64\u8fc7\u7a0b\u4e2d，\u9700\u8981\u786e\u4fdd\u8fd0\u884c\u73af\u5883\u4e0e\u8bad\u7ec3\u73af\u5883\u5728\u4f9d\u8d56\u7248\u672c\u548c\u914d\u7f6e\u65b9\u9762\u4fdd\u6301\u4e00\u81f4\u3002<\/p>\n

\u5728\u751f\u4ea7\u73af\u5883\u4e2d，\u6301\u7eed\u76d1\u63a7\u6a21\u578b\u6027\u80fd\u3001\u6309\u9700\u91cd\u8bad\u7ec3\u5e76\u5728\u4e0a\u7ebf\u524d\u9a8c\u8bc1\u53d8\u5316\u662f\u7a33\u5065MLOps\u5b9e\u8df5\u7684\u91cd\u8981\u7ec4\u6210\u90e8\u5206\u3002scikit-learn\u5f3a\u8c03\u7b80\u5355\u3001\u53ef\u7ec4\u5408\u7684\u7ec4\u4ef6，\u4fbf\u4e8e\u5de5\u7a0b\u5e08\u5c06\u7cbe\u529b\u96c6\u4e2d\u5728\u6570\u636e\u8d28\u91cf\u3001\u8bc4\u4f30\u7b56\u7565\u548c\u81ea\u52a8\u5316\u6d41\u7a0b\u4e0a，\u800c\u4e0d\u5fc5\u8fc7\u591a\u5173\u6ce8\u5e95\u5c42\u5b9e\u73b0\u7ec6\u8282\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"

Scikit-learn：Python\u4e2d\u7684\u5b9e\u7528\u673a\u5668\u5b66\u4e60\u5e93\u672c\u6587\u9762\u5411\u5de5\u7a0b\u5b9e\u8df5\u4ecb\u7ecdPython\u673a\u5668\u5b66\u4e60\u5e93scikit-learn，\u5305\u62ec\u5176\u8bbe\u8ba1\u7406\u5ff5\u3001\u6838\u5fc3API\u7ea6\u5b9a\u4ee5\u53ca\u4e3b\u8981\u4f30\u8ba1\u5668\u5bb6\u65cf\u3002\u540c\u65f6\u8ba8\u8bba\u6570\u636e\u9884\u5904\u7406\u3001\u6a21\u578b\u9009\u62e9\u548c\u8bc4\u4f30\u7b49\u5178\u578b\u5de5\u4f5c\u6d41\u7a0b，\u5e76\u901a\u8fc7\u793a\u610f\u56fe\u8868\u8bf4\u660e\u5e38\u89c1\u7528\u6cd5\u3002\u6587\u4e2d\u793a\u4f8b\u4ec5\u4e3a\u793a\u610f，\u4e0d\u4ee3\u8868\u5177\u4f53\u4ea7\u54c1\u6216\u6807\u51c6\u6027\u80fd\u6307\u6807\u3002\u56fe1：\u82e5\u5e72scikit-learn\u5206\u7c7b\u5668\u5728\u793a\u610f\u6570\u636e\u96c6\u4e0a\u7684Accuracy\u5bf9\u6bd4（\u793a\u610f&#xff09<\/p>\n","protected":false},"author":2,"featured_media":90839,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[66,737,50,207],"topic":[],"class_list":["post-90842","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-server","tag-ai","tag-scikit-learn","tag-50","tag-207"],"yoast_head":"\n[\u4eba\u5de5\u667a\u80fd]Scikit-learn\uff1aPython\u4e2d\u7684\u5b9e\u7528\u673a\u5668\u5b66\u4e60\u5e93 - \u7f51\u7855\u4e92\u8054\u5e2e\u52a9\u4e2d\u5fc3<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.wsisp.com\/helps\/90842.html\" \/>\n<meta property=\"og:locale\" content=\"zh_CN\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"[\u4eba\u5de5\u667a\u80fd]Scikit-learn\uff1aPython\u4e2d\u7684\u5b9e\u7528\u673a\u5668\u5b66\u4e60\u5e93 - \u7f51\u7855\u4e92\u8054\u5e2e\u52a9\u4e2d\u5fc3\" \/>\n<meta property=\"og:description\" content=\"Scikit-learn：Python\u4e2d\u7684\u5b9e\u7528\u673a\u5668\u5b66\u4e60\u5e93\u672c\u6587\u9762\u5411\u5de5\u7a0b\u5b9e\u8df5\u4ecb\u7ecdPython\u673a\u5668\u5b66\u4e60\u5e93scikit-learn，\u5305\u62ec\u5176\u8bbe\u8ba1\u7406\u5ff5\u3001\u6838\u5fc3API\u7ea6\u5b9a\u4ee5\u53ca\u4e3b\u8981\u4f30\u8ba1\u5668\u5bb6\u65cf\u3002\u540c\u65f6\u8ba8\u8bba\u6570\u636e\u9884\u5904\u7406\u3001\u6a21\u578b\u9009\u62e9\u548c\u8bc4\u4f30\u7b49\u5178\u578b\u5de5\u4f5c\u6d41\u7a0b，\u5e76\u901a\u8fc7\u793a\u610f\u56fe\u8868\u8bf4\u660e\u5e38\u89c1\u7528\u6cd5\u3002\u6587\u4e2d\u793a\u4f8b\u4ec5\u4e3a\u793a\u610f，\u4e0d\u4ee3\u8868\u5177\u4f53\u4ea7\u54c1\u6216\u6807\u51c6\u6027\u80fd\u6307\u6807\u3002\u56fe1：\u82e5\u5e72scikit-learn\u5206\u7c7b\u5668\u5728\u793a\u610f\u6570\u636e\u96c6\u4e0a\u7684Accuracy\u5bf9\u6bd4（\u793a\u610f&#xff09\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.wsisp.com\/helps\/90842.html\" \/>\n<meta property=\"og:site_name\" content=\"\u7f51\u7855\u4e92\u8054\u5e2e\u52a9\u4e2d\u5fc3\" \/>\n<meta property=\"article:published_time\" content=\"2026-08-05T22:48:45+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2026\/08\/20260805224844-6a73bdcc07e92.png\" \/>\n<meta name=\"author\" content=\"admin\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"\u4f5c\u8005\" \/>\n\t<meta name=\"twitter:data1\" content=\"admin\" \/>\n\t<meta name=\"twitter:label2\" content=\"\u9884\u8ba1\u9605\u8bfb\u65f6\u95f4\" \/>\n\t<meta name=\"twitter:data2\" content=\"1 \u5206\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/www.wsisp.com\/helps\/90842.html\",\"url\":\"https:\/\/www.wsisp.com\/helps\/90842.html\",\"name\":\"[\u4eba\u5de5\u667a\u80fd]Scikit-learn\uff1aPython\u4e2d\u7684\u5b9e\u7528\u673a\u5668\u5b66\u4e60\u5e93 - \u7f51\u7855\u4e92\u8054\u5e2e\u52a9\u4e2d\u5fc3\",\"isPartOf\":{\"@id\":\"https:\/\/www.wsisp.com\/helps\/#website\"},\"datePublished\":\"2026-08-05T22:48:45+00:00\",\"dateModified\":\"2026-08-05T22:48:45+00:00\",\"author\":{\"@id\":\"https:\/\/www.wsisp.com\/helps\/#\/schema\/person\/358e386c577a3ab51c4493330a20ad41\"},\"breadcrumb\":{\"@id\":\"https:\/\/www.wsisp.com\/helps\/90842.html#breadcrumb\"},\"inLanguage\":\"zh-Hans\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/www.wsisp.com\/helps\/90842.html\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/www.wsisp.com\/helps\/90842.html#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"\u9996\u9875\",\"item\":\"https:\/\/www.wsisp.com\/helps\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"[\u4eba\u5de5\u667a\u80fd]Scikit-learn\uff1aPython\u4e2d\u7684\u5b9e\u7528\u673a\u5668\u5b66\u4e60\u5e93\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/www.wsisp.com\/helps\/#website\",\"url\":\"https:\/\/www.wsisp.com\/helps\/\",\"name\":\"\u7f51\u7855\u4e92\u8054\u5e2e\u52a9\u4e2d\u5fc3\",\"description\":\"\u9999\u6e2f\u670d\u52a1\u5668_\u9999\u6e2f\u4e91\u670d\u52a1\u5668\u8d44\u8baf_\u670d\u52a1\u5668\u5e2e\u52a9\u6587\u6863_\u670d\u52a1\u5668\u6559\u7a0b\",\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\/\/www.wsisp.com\/helps\/?s={search_term_string}\"},\"query-input\":\"required name=search_term_string\"}],\"inLanguage\":\"zh-Hans\"},{\"@type\":\"Person\",\"@id\":\"https:\/\/www.wsisp.com\/helps\/#\/schema\/person\/358e386c577a3ab51c4493330a20ad41\",\"name\":\"admin\",\"image\":{\"@type\":\"ImageObject\",\"inLanguage\":\"zh-Hans\",\"@id\":\"https:\/\/www.wsisp.com\/helps\/#\/schema\/person\/image\/\",\"url\":\"https:\/\/gravatar.wp-china-yes.net\/avatar\/?s=96&d=mystery\",\"contentUrl\":\"https:\/\/gravatar.wp-china-yes.net\/avatar\/?s=96&d=mystery\",\"caption\":\"admin\"},\"sameAs\":[\"http:\/\/wp.wsisp.com\"],\"url\":\"https:\/\/www.wsisp.com\/helps\/author\/admin\"}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"[\u4eba\u5de5\u667a\u80fd]Scikit-learn\uff1aPython\u4e2d\u7684\u5b9e\u7528\u673a\u5668\u5b66\u4e60\u5e93 - \u7f51\u7855\u4e92\u8054\u5e2e\u52a9\u4e2d\u5fc3","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/www.wsisp.com\/helps\/90842.html","og_locale":"zh_CN","og_type":"article","og_title":"[\u4eba\u5de5\u667a\u80fd]Scikit-learn\uff1aPython\u4e2d\u7684\u5b9e\u7528\u673a\u5668\u5b66\u4e60\u5e93 - \u7f51\u7855\u4e92\u8054\u5e2e\u52a9\u4e2d\u5fc3","og_description":"Scikit-learn：Python\u4e2d\u7684\u5b9e\u7528\u673a\u5668\u5b66\u4e60\u5e93\u672c\u6587\u9762\u5411\u5de5\u7a0b\u5b9e\u8df5\u4ecb\u7ecdPython\u673a\u5668\u5b66\u4e60\u5e93scikit-learn，\u5305\u62ec\u5176\u8bbe\u8ba1\u7406\u5ff5\u3001\u6838\u5fc3API\u7ea6\u5b9a\u4ee5\u53ca\u4e3b\u8981\u4f30\u8ba1\u5668\u5bb6\u65cf\u3002\u540c\u65f6\u8ba8\u8bba\u6570\u636e\u9884\u5904\u7406\u3001\u6a21\u578b\u9009\u62e9\u548c\u8bc4\u4f30\u7b49\u5178\u578b\u5de5\u4f5c\u6d41\u7a0b，\u5e76\u901a\u8fc7\u793a\u610f\u56fe\u8868\u8bf4\u660e\u5e38\u89c1\u7528\u6cd5\u3002\u6587\u4e2d\u793a\u4f8b\u4ec5\u4e3a\u793a\u610f，\u4e0d\u4ee3\u8868\u5177\u4f53\u4ea7\u54c1\u6216\u6807\u51c6\u6027\u80fd\u6307\u6807\u3002\u56fe1：\u82e5\u5e72scikit-learn\u5206\u7c7b\u5668\u5728\u793a\u610f\u6570\u636e\u96c6\u4e0a\u7684Accuracy\u5bf9\u6bd4（\u793a\u610f&#xff09","og_url":"https:\/\/www.wsisp.com\/helps\/90842.html","og_site_name":"\u7f51\u7855\u4e92\u8054\u5e2e\u52a9\u4e2d\u5fc3","article_published_time":"2026-08-05T22:48:45+00:00","og_image":[{"url":"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2026\/08\/20260805224844-6a73bdcc07e92.png"}],"author":"admin","twitter_card":"summary_large_image","twitter_misc":{"\u4f5c\u8005":"admin","\u9884\u8ba1\u9605\u8bfb\u65f6\u95f4":"1 \u5206"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"WebPage","@id":"https:\/\/www.wsisp.com\/helps\/90842.html","url":"https:\/\/www.wsisp.com\/helps\/90842.html","name":"[\u4eba\u5de5\u667a\u80fd]Scikit-learn\uff1aPython\u4e2d\u7684\u5b9e\u7528\u673a\u5668\u5b66\u4e60\u5e93 - \u7f51\u7855\u4e92\u8054\u5e2e\u52a9\u4e2d\u5fc3","isPartOf":{"@id":"https:\/\/www.wsisp.com\/helps\/#website"},"datePublished":"2026-08-05T22:48:45+00:00","dateModified":"2026-08-05T22:48:45+00:00","author":{"@id":"https:\/\/www.wsisp.com\/helps\/#\/schema\/person\/358e386c577a3ab51c4493330a20ad41"},"breadcrumb":{"@id":"https:\/\/www.wsisp.com\/helps\/90842.html#breadcrumb"},"inLanguage":"zh-Hans","potentialAction":[{"@type":"ReadAction","target":["https:\/\/www.wsisp.com\/helps\/90842.html"]}]},{"@type":"BreadcrumbList","@id":"https:\/\/www.wsisp.com\/helps\/90842.html#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"\u9996\u9875","item":"https:\/\/www.wsisp.com\/helps"},{"@type":"ListItem","position":2,"name":"[\u4eba\u5de5\u667a\u80fd]Scikit-learn\uff1aPython\u4e2d\u7684\u5b9e\u7528\u673a\u5668\u5b66\u4e60\u5e93"}]},{"@type":"WebSite","@id":"https:\/\/www.wsisp.com\/helps\/#website","url":"https:\/\/www.wsisp.com\/helps\/","name":"\u7f51\u7855\u4e92\u8054\u5e2e\u52a9\u4e2d\u5fc3","description":"\u9999\u6e2f\u670d\u52a1\u5668_\u9999\u6e2f\u4e91\u670d\u52a1\u5668\u8d44\u8baf_\u670d\u52a1\u5668\u5e2e\u52a9\u6587\u6863_\u670d\u52a1\u5668\u6559\u7a0b","potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/www.wsisp.com\/helps\/?s={search_term_string}"},"query-input":"required name=search_term_string"}],"inLanguage":"zh-Hans"},{"@type":"Person","@id":"https:\/\/www.wsisp.com\/helps\/#\/schema\/person\/358e386c577a3ab51c4493330a20ad41","name":"admin","image":{"@type":"ImageObject","inLanguage":"zh-Hans","@id":"https:\/\/www.wsisp.com\/helps\/#\/schema\/person\/image\/","url":"https:\/\/gravatar.wp-china-yes.net\/avatar\/?s=96&d=mystery","contentUrl":"https:\/\/gravatar.wp-china-yes.net\/avatar\/?s=96&d=mystery","caption":"admin"},"sameAs":["http:\/\/wp.wsisp.com"],"url":"https:\/\/www.wsisp.com\/helps\/author\/admin"}]}},"_links":{"self":[{"href":"https:\/\/www.wsisp.com\/helps\/wp-json\/wp\/v2\/posts\/90842","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.wsisp.com\/helps\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.wsisp.com\/helps\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.wsisp.com\/helps\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.wsisp.com\/helps\/wp-json\/wp\/v2\/comments?post=90842"}],"version-history":[{"count":0,"href":"https:\/\/www.wsisp.com\/helps\/wp-json\/wp\/v2\/posts\/90842\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.wsisp.com\/helps\/wp-json\/wp\/v2\/media\/90839"}],"wp:attachment":[{"href":"https:\/\/www.wsisp.com\/helps\/wp-json\/wp\/v2\/media?parent=90842"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.wsisp.com\/helps\/wp-json\/wp\/v2\/categories?post=90842"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.wsisp.com\/helps\/wp-json\/wp\/v2\/tags?post=90842"},{"taxonomy":"topic","embeddable":true,"href":"https:\/\/www.wsisp.com\/helps\/wp-json\/wp\/v2\/topic?post=90842"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}