{"id":109728,"date":"2026-09-24T18:41:37","date_gmt":"2026-09-24T10:41:37","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/109728.html"},"modified":"2026-09-24T18:41:37","modified_gmt":"2026-09-24T10:41:37","slug":"%e6%a8%a1%e5%9e%8b%e6%b5%8b%e8%af%95-95%ef%bc%8c%e4%b8%80%e4%b8%8a%e7%ba%bf%e6%8e%89%e5%88%b0-60%ef%bc%9f%e5%85%88%e6%a3%80%e6%9f%a5%e8%bf%99%e4%b8%89%e5%a4%84%e6%95%b0%e6%8d%ae%e6%b3%84%e6%bc%8f","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/109728.html","title":{"rendered":"\u6a21\u578b\u6d4b\u8bd5 95%\uff0c\u4e00\u4e0a\u7ebf\u6389\u5230 60%\uff1f\u5148\u68c0\u67e5\u8fd9\u4e09\u5904\u6570\u636e\u6cc4\u6f0f"},"content":{"rendered":"

\u6a21\u578b\u6d4b\u8bd5 95%，\u4e00\u4e0a\u7ebf\u6389\u5230 60%？\u5148\u68c0\u67e5\u8fd9\u4e09\u5904\u6570\u636e\u6cc4\u6f0f<\/h2>\n

\u6a21\u578b\u5728\u6d4b\u8bd5\u96c6\u4e0a\u8dd1\u51fa 95% \u7684\u51c6\u786e\u7387，\u8bc4\u5ba1\u4f1a\u4e0a\u5927\u5bb6\u90fd\u633a\u6ee1\u610f\u3002\u4e0a\u7ebf\u4e00\u5468，\u5b9e\u9645\u6548\u679c\u6389\u5230 60% \u51fa\u5934 \u2014\u2014 \u4e1a\u52a1\u65b9\u95ee\u300c\u4f60\u4eec\u4e0d\u662f\u8bf4 95% \u5417\u300d，\u4f60\u56de\u53bb\u7ffb\u4ee3\u7801，\u67e5\u4e86\u534a\u5929，\u6700\u540e\u53d1\u73b0\u95ee\u9898\u4e0d\u5728\u6a21\u578b，\u800c\u5728\u6570\u636e\u5212\u5206\u90a3\u51e0\u884c\u3002\u8fd9\u79cd\u60c5\u51b5\u5341\u6709\u516b\u4e5d\u662f \u6570\u636e\u6cc4\u6f0f（Data Leakage）：\u6d4b\u8bd5\u96c6\u7684\u4fe1\u606f\u5728\u8bad\u7ec3\u9636\u6bb5\u5c31\u88ab\u6a21\u578b\u300c\u770b\u89c1\u300d\u4e86\u3002\u6d4b\u8bd5\u5206\u6570\u81ea\u7136\u597d\u770b，\u4f46\u5b83\u6d4b\u7684\u4e0d\u662f\u771f\u5b9e\u6c34\u5e73，\u800c\u662f\u6a21\u578b\u5bf9\u300c\u5df2\u7ecf\u89c1\u8fc7\u7684\u6570\u636e\u300d\u7684\u8bb0\u5fc6\u3002\u66f4\u9ebb\u70e6\u7684\u662f：\u6570\u636e\u6cc4\u6f0f\u4e0d\u4f1a\u62a5\u9519\u3002\u4ee3\u7801\u8dd1\u5f97\u901a\u3001\u6307\u6807\u8fd8\u6f02\u4eae，\u4f60\u6ca1\u6709\u4efb\u4f55\u63d0\u793a \u2014\u2014 \u76f4\u5230\u4e0a\u7ebf\u3002\u8fd9\u4e09\u4e2a\u573a\u666f\u770b\u7740\u5404\u4e0d\u76f8\u540c，\u6839\u6e90\u5374\u662f\u540c\u4e00\u4e2a：\u5212\u5206\u6570\u636e\u65f6，\u6ca1\u6709\u628a\u300c\u8bad\u7ec3\u9636\u6bb5\u5230\u5e95\u80fd\u770b\u5230\u54ea\u4e9b\u4fe1\u606f\u300d\u60f3\u6e05\u695a\u3002<\/p>\n

\u573a\u666f\u4e00：\u5148\u505a\u4e86\u6807\u51c6\u5316 \/ \u5f52\u4e00\u5316，\u518d\u5212\u5206\u6570\u636e\u96c6<\/h3>\n

\u8fd9\u662f\u6700\u5e38\u89c1\u7684\u4e00\u4e2a \u2014\u2014 \u5199\u6cd5\u4e0a\u6700\u987a\u7406\u6210\u7ae0，\u4e5f\u6700\u4e0d\u5bb9\u6613\u8ba9\u4eba\u89c9\u5f97\u6709\u95ee\u9898：<\/p>\n

from<\/span> sklearn.<\/span>preprocessing import<\/span> StandardScaler # \u6807\u51c6\u5316\u5de5\u5177：\u628a\u6bcf\u5217\u6362\u7b97\u6210\u5747\u503c 0\u3001\u6807\u51c6\u5dee 1<\/span>
\nfrom<\/span> sklearn.<\/span>model_selection import<\/span> train_test_split # \u5207\u5206\u5de5\u5177：\u6309\u6bd4\u4f8b\u5212\u5206\u8bad\u7ec3\u96c6 \/ \u6d4b\u8bd5\u96c6<\/span><\/p>\n

scaler =<\/span> StandardScaler(<\/span>)<\/span> # \u274c \u5148\u9020\u4e00\u4e2a\u6807\u51c6\u5316\u5668<\/span>
\nX_scaled =<\/span> scaler.<\/span>fit_transform(<\/span>X)<\/span> # \u274c \u95ee\u9898\u5728\u8fd9\u4e00\u884c：fit \u4f1a\u300c\u4ece\u6570\u636e\u91cc\u5b66\u53c2\u6570\u300d（\u6bcf\u5217\u7684\u5747\u503c\u548c\u6807\u51c6\u5dee），<\/span>
\n # \u800c\u5b83\u7684\u8f93\u5165 X \u662f\u300c\u5168\u91cf\u6570\u636e\u300d\u2014\u2014 \u6d4b\u8bd5\u96c6\u4e5f\u88ab\u4e00\u8d77\u7b97\u8fdb\u53bb\u4e86<\/span><\/p>\n

X_train,<\/span> X_test,<\/span> y_train,<\/span> y_test =<\/span> train_test_split(<\/span>X_scaled,<\/span> y,<\/span> test_size=<\/span>0.3<\/span>,<\/span> random_state=<\/span>42<\/span>)<\/span>
\n # \u274c \u5230\u8fd9\u4e00\u6b65\u624d\u5207\u5206：\u6d4b\u8bd5\u96c6\u7684\u4fe1\u606f\u5728\u4e0a\u4e00\u884c\u5c31\u6f0f\u8fdb\u8bad\u7ec3\u8fc7\u7a0b\u4e86<\/span><\/p>\n

\u4e3a\u4ec0\u4e48\u9519：fit_transform \u8981\u8ba1\u7b97\u6bcf\u4e00\u5217\u7684\u5747\u503c\u548c\u6807\u51c6\u5dee，\u800c\u5b83\u7b97\u7684\u65f6\u5019\u7528\u7684\u662f\u6574\u4efd\u6570\u636e \u2014\u2014 \u5305\u62ec\u540e\u9762\u8981\u5f53\u6d4b\u8bd5\u96c6\u7684\u90a3 30%\u3002\u4e5f\u5c31\u662f\u8bf4，\u8bad\u7ec3\u65f6\u7528\u5230\u7684\u300c\u5217\u5747\u503c\u300d，\u91cc\u9762\u6df7\u8fdb\u4e86\u6d4b\u8bd5\u96c6\u6837\u672c\u7684\u8d21\u732e\u3002\u6d4b\u8bd5\u96c6\u7684\u4fe1\u606f\u987a\u7740\u8fd9\u4e2a\u5747\u503c\u6f0f\u8fdb\u4e86\u8bad\u7ec3\u8fc7\u7a0b\u3002\u6b63\u786e\u5199\u6cd5：\u5148\u5212\u5206，fit \u53ea\u5728\u8bad\u7ec3\u96c6\u4e0a\u505a，\u6d4b\u8bd5\u96c6\u53ea transform：<\/p>\n

# \u2705 \u6b63\u786e：\u7b2c\u4e00\u6b65\u5c31\u662f\u5207\u5206<\/span>
\nX_train,<\/span> X_test,<\/span> y_train,<\/span> y_test =<\/span> train_test_split(<\/span>
\n X,<\/span> y,<\/span> # X \u662f\u7279\u5f81\u3001y \u662f\u6807\u7b7e，\u4e24\u4e2a\u4e00\u8d77\u5207，\u4fdd\u8bc1\u6837\u672c\u548c\u6807\u7b7e\u4e00\u4e00\u5bf9\u5e94<\/span>
\n test_size=<\/span>0.3<\/span>,<\/span> # \u6d4b\u8bd5\u96c6\u5360 30%<\/span>
\n random_state=<\/span>42<\/span>,<\/span> # \u56fa\u5b9a\u968f\u673a\u79cd\u5b50：\u6bcf\u6b21\u8dd1\u90fd\u5f97\u5230\u540c\u4e00\u5957\u5212\u5206，\u4e24\u6b21\u5b9e\u9a8c\u624d\u53ef\u6bd4<\/span>
\n)<\/span><\/p>\n

scaler =<\/span> StandardScaler(<\/span>)<\/span> # \u9020\u4e00\u4e2a\u6807\u51c6\u5316\u5668<\/span>
\nX_train =<\/span> scaler.<\/span>fit_transform(<\/span>X_train)<\/span> # \u53ea\u5728\u8bad\u7ec3\u96c6\u4e0a\u300c\u5b66\u300d\u53c2\u6570（\u5747\u503c\u3001\u6807\u51c6\u5dee）<\/span>
\nX_test =<\/span> scaler.<\/span>transform(<\/span>X_test)<\/span> # \u6d4b\u8bd5\u96c6\u53ea\u5957\u7528\u8bad\u7ec3\u96c6\u5b66\u5230\u7684\u53c2\u6570，\u81ea\u5df1\u4e0d\u53c2\u4e0e\u8ba1\u7b97<\/span><\/p>\n

\u540c\u4e00\u4e2a\u9053\u7406\u9002\u7528\u4e8e\u6240\u6709\u300c\u4ece\u6570\u636e\u91cc\u5b66\u53c2\u6570\u300d\u7684\u9884\u5904\u7406：\u5f52\u4e00\u5316\u3001\u6807\u51c6\u5316\u3001\u586b\u5145\u7f3a\u5931\u503c（SimpleImputer）\u3001\u7279\u5f81\u9009\u62e9\u3001PCA \u964d\u7ef4\u3001\u76ee\u6807\u7f16\u7801（\u7528\u6807\u7b7e\u7684\u7edf\u8ba1\u91cf\u7ed9\u7c7b\u522b\u7279\u5f81\u7f16\u7801 \u2014\u2014 \u8fd9\u4e00\u7c7b\u6cc4\u6f0f\u5f97\u6700\u9690\u853d，\u52a1\u5fc5\u53ea\u5728\u8bad\u7ec3\u96c6\u4e0a\u7b97）\u2014\u2014 \u51e1\u662f fit \u5f00\u5934\u7684，\u90fd\u53ea\u80fd\u5728\u8bad\u7ec3\u96c6\u4e0a\u505a\u3002\u4e00\u53e5\u8bdd\u8bb0：\u8bad\u7ec3\u96c6\u8d1f\u8d23\u300c\u5b66\u300d，\u6d4b\u8bd5\u96c6\u53ea\u8d1f\u8d23\u300c\u88ab\u53d8\u6362\u300d\u3002<\/p>\n

\u573a\u666f\u4e8c：\u6309\u884c\u5212\u5206，\u628a\u540c\u4e00\u4e2a\u5b9e\u4f53\u7684\u591a\u6761\u8bb0\u5f55\u5206\u5230\u4e86\u4e24\u8fb9<\/h3>\n

\u8fd9\u4e2a\u573a\u666f\u5728\u771f\u5b9e\u4e1a\u52a1\u6570\u636e\u91cc\u7279\u522b\u5e38\u89c1，\u56e0\u4e3a\u6570\u636e\u5f80\u5f80\u300c\u4e00\u4eba\u591a\u884c\u300d\u3002\u6bd4\u5982\u4e00\u4efd\u5c31\u8bca\u8bb0\u5f55\u8868，\u540c\u4e00\u4e2a\u60a3\u8005\u6709 5 \u6b21\u5c31\u8bca\u8bb0\u5f55\u3002\u5982\u679c\u76f4\u63a5\u6309\u884c\u968f\u673a\u5212\u5206：<\/p>\n

# \u274c \u9519\u8bef：\u6309\u884c\u968f\u673a\u5212\u5206<\/span>
\nX_train,<\/span> X_test,<\/span> y_train,<\/span> y_test =<\/span> train_test_split(<\/span>
\n X,<\/span> y,<\/span> # \u7279\u5f81\u548c\u6807\u7b7e\u4e00\u8d77\u5207（\u8fd9\u4e00\u6b65\u672c\u8eab\u7684\u7528\u6cd5\u6ca1\u9519）<\/span>
\n test_size=<\/span>0.3<\/span>,<\/span> # \u6d4b\u8bd5\u96c6\u5360 30%<\/span>
\n random_state=<\/span>42<\/span>,<\/span> # \u53ef\u590d\u73b0 \u2014\u2014 \u4f46\u300c\u6309\u884c\u5207\u300d\u8fd9\u4ef6\u4e8b\u672c\u8eab\u5c31\u662f\u95ee\u9898<\/span>
\n)<\/span>
\n# \u540c\u4e00\u4e2a\u60a3\u8005\u7684\u591a\u6761\u8bb0\u5f55，\u4f1a\u88ab\u968f\u673a\u5206\u5230\u8bad\u7ec3\u96c6\u548c\u6d4b\u8bd5\u96c6\u4e24\u8fb9<\/span><\/p>\n

5 \u6761\u8bb0\u5f55\u5f88\u53ef\u80fd 3 \u6761\u5206\u5230\u8bad\u7ec3\u96c6\u30012 \u6761\u5206\u5230\u6d4b\u8bd5\u96c6\u3002\u800c\u540c\u4e00\u4e2a\u60a3\u8005\u7684\u591a\u6b21\u5c31\u8bca\u8bb0\u5f55\u5728\u7279\u5f81\u4e0a\u9ad8\u5ea6\u76f8\u4f3c（\u5e74\u9f84\u3001\u57fa\u7840\u75c5\u3001\u65e2\u5f80\u53f2\u51e0\u4e4e\u4e00\u6837）\u2014\u2014\u6a21\u578b\u5728\u8bad\u7ec3\u65f6\u5df2\u7ecf\u89c1\u8fc7\u8fd9\u4e2a\u4eba\u7684\u300c\u753b\u50cf\u300d\u4e86，\u6d4b\u8bd5\u96c6\u91cc\u7684\u90a3\u4e24\u6761\u5f53\u7136\u5bb9\u6613\u5224\u5bf9\u3002 \u4f46\u8fd9\u4e0d\u4ee3\u8868\u6a21\u578b\u5bf9\u65b0\u60a3\u8005\u6709\u5224\u65ad\u529b\u3002\u6b63\u786e\u5199\u6cd5：\u6309\u5b9e\u4f53\u5212\u5206，\u4fdd\u8bc1\u540c\u4e00\u4e2a\u60a3\u8005（\u6216\u7528\u6237\u3001\u8bbe\u5907\u3001\u95e8\u5e97）\u53ea\u51fa\u73b0\u5728\u4e00\u8fb9：<\/p>\n

# \u2705 \u6b63\u786e：\u6309\u60a3\u8005 ID \u5212\u5206<\/span>
\nfrom<\/span> sklearn.<\/span>model_selection import<\/span> GroupShuffleSplit # \u652f\u6301\u300c\u6309\u7ec4\u5207\u300d\u7684\u5212\u5206\u5668<\/span><\/p>\n

gss =<\/span> GroupShuffleSplit(<\/span>
\n n_splits=<\/span>1<\/span>,<\/span> # \u53ea\u5207\u4e00\u6b21（\u53cd\u590d\u5207\u5206\u53d6\u5e73\u5747\u662f\u53e6\u4e00\u56de\u4e8b）<\/span>
\n test_size=<\/span>0.3<\/span>,<\/span> # \u6d4b\u8bd5\u96c6\u5360 30%<\/span>
\n random_state=<\/span>42<\/span>,<\/span> # \u56fa\u5b9a\u79cd\u5b50，\u7ed3\u679c\u53ef\u590d\u73b0<\/span>
\n)<\/span>
\ntrain_idx,<\/span> test_idx =<\/span> next<\/span>(<\/span>gss.<\/span>split(<\/span>
\n X,<\/span> y,<\/span> # \u7279\u5f81\u548c\u6807\u7b7e\u4e00\u8d77\u4f20\u8fdb\u53bb<\/span>
\n groups=<\/span>df[<\/span>"\u60a3\u8005ID"<\/span>]<\/span>,<\/span> # \u2605 \u5173\u952e：\u544a\u8bc9\u5b83\u300c\u54ea\u4e9b\u884c\u5c5e\u4e8e\u540c\u4e00\u4e2a\u60a3\u8005\u300d<\/span>
\n)<\/span>)<\/span>
\n# \u540c\u4e00\u4e2a\u60a3\u8005\u7684\u6240\u6709\u8bb0\u5f55，\u4f1a\u6574\u4f53\u843d\u5230\u540c\u4e00\u8fb9<\/span><\/p>\n

X_train,<\/span> X_test =<\/span> X.<\/span>iloc[<\/span>train_idx]<\/span>,<\/span> X.<\/span>iloc[<\/span>test_idx]<\/span> # \u6309\u884c\u53f7\u53d6\u51fa\u8bad\u7ec3 \/ \u6d4b\u8bd5\u7279\u5f81<\/span>
\ny_train,<\/span> y_test =<\/span> y.<\/span>iloc[<\/span>train_idx]<\/span>,<\/span> y.<\/span>iloc[<\/span>test_idx]<\/span> # \u6807\u7b7e\u7528\u540c\u4e00\u5957\u884c\u53f7\u53d6，\u907f\u514d\u9519\u4f4d<\/span><\/p>\n

GroupShuffleSplit \u7684 groups \u53c2\u6570\u5c31\u662f\u5e72\u8fd9\u4e2a\u7684：\u540c\u4e00\u4e2a group \u7684\u6837\u672c\u8981\u4e48\u5168\u5728\u8bad\u7ec3\u96c6\u3001\u8981\u4e48\u5168\u5728\u6d4b\u8bd5\u96c6\u3002（\u4e0a\u9762\u8fd9\u6bb5\u91cc X \u662f DataFrame，\u6240\u4ee5\u7528 .iloc \u6309\u884c\u53f7\u53d6；\u5982\u679c X \u662f\u7eaf numpy \u6570\u7ec4，\u76f4\u63a5\u5199 X[train_idx] \u5c31\u884c\u3002）<\/p>\n

\u8981\u5212\u5206\u7684\u300c\u5b9e\u4f53\u300d\u662f\u4ec0\u4e48？\u770b\u4e1a\u52a1：\u98ce\u63a7\u6309\u5ba2\u6237\u3001\u63a8\u8350\u6309\u7528\u6237\u3001\u533b\u7597\u6309\u60a3\u8005\u3001\u8bbe\u5907\u9884\u6d4b\u6309\u8bbe\u5907\u53f7\u3002\u5224\u65ad\u6807\u51c6\u662f\u300c\u54ea\u4e9b\u884c\u5176\u5b9e\u63cf\u8ff0\u7684\u662f\u540c\u4e00\u4e2a\u5bf9\u8c61\u300d\u3002<\/p>\n

\u573a\u666f\u4e09：\u65f6\u95f4\u5e8f\u5217\u7528\u672a\u6765\u6570\u636e\u8bad\u7ec3，\u53bb\u9884\u6d4b\u8fc7\u53bb<\/h3>\n

\u5982\u679c\u6570\u636e\u5e26\u65f6\u95f4\u5c5e\u6027，\u6309\u884c\u968f\u673a\u5212\u5206\u57fa\u672c\u4e00\u5b9a\u4f1a\u51fa\u9519：<\/p>\n

# \u274c \u9519\u8bef：\u65f6\u95f4\u6570\u636e\u968f\u673a\u5212\u5206<\/span>
\nX_train,<\/span> X_test,<\/span> y_train,<\/span> y_test =<\/span> train_test_split(<\/span>
\n X,<\/span> y,<\/span> # \u7279\u5f81\u548c\u6807\u7b7e\u4e00\u8d77\u5207<\/span>
\n test_size=<\/span>0.3<\/span>,<\/span> # \u6d4b\u8bd5\u96c6\u5360 30%<\/span>
\n random_state=<\/span>42<\/span>,<\/span> # \u56fa\u5b9a\u79cd\u5b50（\u53ef\u590d\u73b0 \u2014\u2014 \u4f46\u95ee\u9898\u4e0d\u5728\u79cd\u5b50，\u5728\u300c\u968f\u673a\u300d\u8fd9\u4ef6\u4e8b）<\/span>
\n)<\/span>
\n# \u300c\u968f\u673a\u300d\u610f\u5473\u7740：6 \u6708\u7684\u6570\u636e\u53ef\u80fd\u8fdb\u8bad\u7ec3\u96c6\u30013 \u6708\u7684\u6570\u636e\u53ef\u80fd\u8fdb\u6d4b\u8bd5\u96c6 \u2014\u2014 \u672a\u6765\u6f0f\u8fdb\u4e86\u8bad\u7ec3<\/span><\/p>\n

\u968f\u673a\u5212\u5206\u4f1a\u8ba9\u672a\u6765\u7684\u6837\u672c\u8fdb\u5165\u8bad\u7ec3\u96c6，\u800c\u6d4b\u8bd5\u96c6\u91cc\u7559\u7740\u8fc7\u53bb\u7684\u6837\u672c\u3002\u6a21\u578b\u5728\u8bad\u7ec3\u65f6\u5df2\u7ecf\u300c\u89c1\u8fc7\u672a\u6765\u300d，\u7528\u5b83\u53bb\u9884\u6d4b\u8fc7\u53bb，\u5206\u6570\u5f53\u7136\u6f02\u4eae \u2014\u2014 \u4f46\u4e0a\u7ebf\u540e\u4f60\u624b\u4e0a\u6c38\u8fdc\u53ea\u6709\u5386\u53f2\u6570\u636e，\u8981\u9884\u6d4b\u7684\u662f\u771f\u6b63\u7684\u672a\u6765\u3002\u6b63\u786e\u5199\u6cd5：\u6309\u65f6\u95f4\u5207\u5206，\u8bad\u7ec3\u96c6\u53d6\u524d\u9762\u4e00\u6bb5\u3001\u6d4b\u8bd5\u96c6\u53d6\u540e\u9762\u4e00\u6bb5：<\/p>\n

# \u2705 \u6b63\u786e：\u6309\u65f6\u95f4\u5207\u5206（\u4ee5 2025-01-01 \u4e3a\u754c）<\/span>
\ntrain =<\/span> df[<\/span>df[<\/span>"\u65e5\u671f"<\/span>]<\/span> <<\/span> "2025-01-01"<\/span>]<\/span> # \u8bad\u7ec3\u96c6：\u5206\u754c\u70b9\u4e4b\u524d\u7684\u5168\u90e8\u5386\u53f2\u6570\u636e<\/span>
\ntest =<\/span> df[<\/span>df[<\/span>"\u65e5\u671f"<\/span>]<\/span> >=<\/span> "2025-01-01"<\/span>]<\/span> # \u6d4b\u8bd5\u96c6：\u5206\u754c\u70b9\u4e4b\u540e\u7684（\u6a21\u62df\u300c\u672a\u6765\u300d）<\/span><\/p>\n

\u66f4\u4e25\u8c28\u4e00\u70b9\u7684\u505a\u6cd5\u662f\u6eda\u52a8\u9a8c\u8bc1（TimeSeriesSplit）：\u7528 1\u20133 \u6708\u8bad\u7ec3\u30014 \u6708\u9a8c\u8bc1，\u518d\u7528 1\u20134 \u6708\u8bad\u7ec3\u30015 \u6708\u9a8c\u8bc1 \u2014\u2014 \u59cb\u7ec8\u4fdd\u8bc1\u300c\u7528\u8fc7\u53bb\u9884\u6d4b\u5c06\u6765\u300d\u3002<\/p>\n

\u4e09\u5904\u6cc4\u6f0f\u5176\u5b9e\u662f\u4e00\u56de\u4e8b<\/h3>\n

\u4e09\u4e2a\u573a\u666f\u7684\u5f62\u6001\u4e0d\u540c \u2014\u2014 \u4e00\u4e2a\u662f\u7edf\u8ba1\u91cf\u6f0f\u4e86（\u573a\u666f\u4e00），\u4e00\u4e2a\u662f\u540c\u4e00\u4e2a\u5b9e\u4f53\u7684\u5176\u4ed6\u8bb0\u5f55\u6f0f\u4e86（\u573a\u666f\u4e8c），\u4e00\u4e2a\u662f\u672a\u6765\u7684\u6570\u636e\u6f0f\u4e86（\u573a\u666f\u4e09）\u2014\u2014 \u4f46\u6839\u6e90\u76f8\u540c：<\/p>\n

\u5212\u5206\u6570\u636e\u65f6，\u8bad\u7ec3\u9636\u6bb5\u62ff\u5230\u4e86\u5b83\u5728\u771f\u5b9e\u573a\u666f\u91cc\u6839\u672c\u62ff\u4e0d\u5230\u7684\u4fe1\u606f\u3002<\/p>\n

\u843d\u5230\u4ee3\u7801\u4e0a，\u5bf9\u5e94\u4e09\u884c：<\/p>\n\n\u68c0\u67e5\u9879\u6b63\u786e\u505a\u6cd5<\/tr>\n\n\n\n\n
\u9884\u5904\u7406<\/td>\n\u6240\u6709 fit \u90fd\u53ea\u5728\u8bad\u7ec3\u96c6\u4e0a\u505a，\u6d4b\u8bd5\u96c6\u53ea transform<\/td>\n<\/tr>\n
\u5b9e\u4f53<\/td>\n\u6309\u5b9e\u4f53（\u7528\u6237 \/ \u60a3\u8005 \/ \u8bbe\u5907\u53f7）\u5212\u5206，\u800c\u4e0d\u662f\u6309\u884c<\/td>\n<\/tr>\n
\u65f6\u95f4<\/td>\n\u6709\u65f6\u95f4\u5c5e\u6027\u65f6\u6309\u65f6\u95f4\u5207\u5206，\u800c\u4e0d\u662f\u968f\u673a\u5207<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\u4e3a\u4ec0\u4e48\u5b83\u6bd4\u522b\u7684\u9519\u8bef\u66f4\u96be\u53d1\u73b0<\/h3>\n

\u6570\u636e\u6cc4\u6f0f\u4e0d\u62a5\u9519，\u4e5f\u4e0d\u5f71\u54cd\u4ee3\u7801\u8fd0\u884c \u2014\u2014 \u5b83\u53ea\u662f\u8ba9\u6a21\u578b\u9009\u578b\u3001\u8c03\u53c2\u3001\u8981\u4e0d\u8981\u4e0a\u7ebf，\u5168\u90fd\u5efa\u7acb\u5728\u540c\u4e00\u4e2a\u5047\u6570\u5b57\u4e0a，\u7b49\u53d1\u73b0\u65f6\u524d\u9762\u6240\u6709\u5224\u65ad\u90fd\u5f97\u91cd\u6765\u3002\u800c\u5b83\u7684\u4fee\u6b63\u6210\u672c，\u5f80\u5f80\u53ea\u662f\u628a\u4e24\u884c\u4ee3\u7801\u6362\u4e2a\u987a\u5e8f\u3002<\/p>\n

\u53cd\u8fc7\u6765\u4e5f\u6210\u7acb：\u5728\u7c7b\u522b\u4e0d\u5e73\u8861\u7684\u4efb\u52a1\u91cc，\u4e00\u4e2a\u6f02\u4eae\u7684\u51c6\u786e\u7387\u672c\u8eab\u5c31\u662f\u8b66\u62a5 \u2014\u2014 \u6b63\u6837\u672c\u53ea\u5360 2% \u65f6，\u4e00\u4e2a\u300c\u6c38\u8fdc\u5224\u8d1f\u300d\u7684\u6a21\u578b\u51c6\u786e\u7387\u4e5f\u6709 98%，\u4f46\u5b83\u4e00\u4e2a\u6b63\u6837\u672c\u90fd\u6293\u4e0d\u5230\u3002<\/p>\n

\u5728\u52a8\u624b\u4f18\u5316\u6a21\u578b\u4e4b\u524d，\u5148\u786e\u8ba4\u90a3\u4e2a 95% \u662f\u4e0d\u662f\u771f\u7684\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"

\u6a21\u578b\u6d4b\u8bd5 95%，\u4e00\u4e0a\u7ebf\u6389\u5230 60%？\u5148\u68c0\u67e5\u8fd9\u4e09\u5904\u6570\u636e\u6cc4\u6f0f
\n\u6a21\u578b\u5728\u6d4b\u8bd5\u96c6\u4e0a\u8dd1\u51fa 95% \u7684\u51c6\u786e\u7387，\u8bc4\u5ba1\u4f1a\u4e0a\u5927\u5bb6\u90fd\u633a\u6ee1\u610f\u3002\u4e0a\u7ebf\u4e00\u5468，\u5b9e\u9645\u6548\u679c\u6389\u5230 60% \u51fa\u5934 \u2014\u2014 \u4e1a\u52a1\u65b9\u95ee\u300c\u4f60\u4eec\u4e0d\u662f\u8bf4 95% \u5417\u300d，\u4f60\u56de\u53bb\u7ffb\u4ee3\u7801，\u67e5\u4e86\u534a\u5929，\u6700\u540e\u53d1\u73b0\u95ee\u9898\u4e0d\u5728\u6a21\u578b，\u800c\u5728\u6570\u636e\u5212\u5206\u90a3\u51e0\u884c\u3002\u8fd9\u79cd\u60c5\u51b5\u5341\u6709\u516b\u4e5d\u662f \u6570\u636e\u6cc4\u6f0f（Data Leakage）：\u6d4b<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[11653,81,3676,50,207],"topic":[],"class_list":["post-109728","post","type-post","status-publish","format-standard","hentry","category-server","tag-11653","tag-python","tag-sklearn","tag-50","tag-207"],"yoast_head":"\n\u6a21\u578b\u6d4b\u8bd5 95%\uff0c\u4e00\u4e0a\u7ebf\u6389\u5230 60%\uff1f\u5148\u68c0\u67e5\u8fd9\u4e09\u5904\u6570\u636e\u6cc4\u6f0f - \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\/109728.html\" \/>\n<meta property=\"og:locale\" content=\"zh_CN\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"\u6a21\u578b\u6d4b\u8bd5 95%\uff0c\u4e00\u4e0a\u7ebf\u6389\u5230 60%\uff1f\u5148\u68c0\u67e5\u8fd9\u4e09\u5904\u6570\u636e\u6cc4\u6f0f - \u7f51\u7855\u4e92\u8054\u5e2e\u52a9\u4e2d\u5fc3\" \/>\n<meta property=\"og:description\" content=\"\u6a21\u578b\u6d4b\u8bd5 95%，\u4e00\u4e0a\u7ebf\u6389\u5230 60%？\u5148\u68c0\u67e5\u8fd9\u4e09\u5904\u6570\u636e\u6cc4\u6f0f \u6a21\u578b\u5728\u6d4b\u8bd5\u96c6\u4e0a\u8dd1\u51fa 95% \u7684\u51c6\u786e\u7387，\u8bc4\u5ba1\u4f1a\u4e0a\u5927\u5bb6\u90fd\u633a\u6ee1\u610f\u3002\u4e0a\u7ebf\u4e00\u5468，\u5b9e\u9645\u6548\u679c\u6389\u5230 60% \u51fa\u5934 \u2014\u2014 \u4e1a\u52a1\u65b9\u95ee\u300c\u4f60\u4eec\u4e0d\u662f\u8bf4 95% \u5417\u300d，\u4f60\u56de\u53bb\u7ffb\u4ee3\u7801，\u67e5\u4e86\u534a\u5929，\u6700\u540e\u53d1\u73b0\u95ee\u9898\u4e0d\u5728\u6a21\u578b，\u800c\u5728\u6570\u636e\u5212\u5206\u90a3\u51e0\u884c\u3002\u8fd9\u79cd\u60c5\u51b5\u5341\u6709\u516b\u4e5d\u662f \u6570\u636e\u6cc4\u6f0f（Data Leakage）：\u6d4b\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.wsisp.com\/helps\/109728.html\" \/>\n<meta property=\"og:site_name\" content=\"\u7f51\u7855\u4e92\u8054\u5e2e\u52a9\u4e2d\u5fc3\" \/>\n<meta property=\"article:published_time\" content=\"2026-09-24T10:41:37+00:00\" \/>\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=\"2 \u5206\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/www.wsisp.com\/helps\/109728.html\",\"url\":\"https:\/\/www.wsisp.com\/helps\/109728.html\",\"name\":\"\u6a21\u578b\u6d4b\u8bd5 95%\uff0c\u4e00\u4e0a\u7ebf\u6389\u5230 60%\uff1f\u5148\u68c0\u67e5\u8fd9\u4e09\u5904\u6570\u636e\u6cc4\u6f0f - 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