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id=\"%E7%A4%BA%E4%BE%8B\">\u793a\u4f8b<\/h4>\n<p>\u4ee5\u4e0b\u662f\u4e00\u4e2a Python \u793a\u4f8b&#xff0c;\u5c55\u793a\u4e86\u6570\u636e\u51c6\u5907\u548c\u6a21\u578b\u8bc4\u4f30\u7684\u5de5\u4f5c\u6d41\u3002\u672c\u793a\u4f8b\u4f7f\u7528 Scikit-learn \u4e2d\u7684\u76ae\u9a6c\u5370\u7b2c\u5b89\u4eba\u7cd6\u5c3f\u75c5\u6570\u636e\u96c6&#xff08;Pima Indian Diabetes dataset&#xff09;\u3002\u9996\u5148&#xff0c;\u6211\u4eec\u5c06\u521b\u5efa\u4e00\u4e2a\u7528\u4e8e\u6807\u51c6\u5316\u6570\u636e\u7684\u7ba1\u9053&#xff1b;\u7136\u540e&#xff0c;\u6784\u5efa\u4e00\u4e2a\u7ebf\u6027\u5224\u522b\u5206\u6790&#xff08;Linear Discriminant Analysis&#xff09;\u6a21\u578b&#xff1b;\u6700\u540e&#xff0c;\u4f7f\u7528 20 \u6298\u4ea4\u53c9\u9a8c\u8bc1\u5bf9\u7ba1\u9053\u8fdb\u884c\u8bc4\u4f30\u3002\u9996\u5148&#xff0c;\u5bfc\u5165\u6240\u9700\u7684\u5e93&#xff1a;<\/p>\n<p>\u63a5\u4e0b\u6765&#xff0c;\u6309\u7167\u4e4b\u524d\u7684\u793a\u4f8b\u52a0\u8f7d\u76ae\u9a6c\u5370\u7b2c\u5b89\u4eba\u7cd6\u5c3f\u75c5\u6570\u636e\u96c6&#xff1a;<\/p>\n<p>\u7136\u540e&#xff0c;\u901a\u8fc7\u4ee5\u4e0b\u4ee3\u7801\u521b\u5efa\u7ba1\u9053&#xff1a;<\/p>\n<p>estimators &#061; []<br \/>\nestimators.append((&#039;standardize&#039;, StandardScaler()))<br \/>\nestimators.append((&#039;lda&#039;, LinearDiscriminantAnalysis()))<br \/>\nmodel &#061; Pipeline(estimators)<\/p>\n<p>\u6700\u540e&#xff0c;\u8bc4\u4f30\u8be5\u7ba1\u9053\u5e76\u8f93\u51fa\u5176\u51c6\u786e\u7387&#xff1a;<\/p>\n<p>kfold &#061; KFold(n_splits&#061;20, random_state&#061;7, shuffle&#061;True)  # \u6dfb\u52a0shuffle&#061;True\u4ee5\u786e\u4fdd\u7ed3\u679c\u53ef\u590d\u73b0&#xff08;Scikit-learn 1.2&#043;\u7248\u672c&#xff09;<br \/>\nresults &#061; cross_val_score(model, X, Y, cv&#061;kfold)<br \/>\nprint(results.mean())<\/p>\n<h5 id=\"%E8%BE%93%E5%87%BA%E7%BB%93%E6%9E%9C\">\u8f93\u51fa\u7ed3\u679c<\/h5>\n<p>plaintext<\/p>\n<p>0.7790148448043184<\/p>\n<p>\u4e0a\u8ff0\u8f93\u51fa\u7ed3\u679c\u662f\u8be5\u8bbe\u7f6e\u5728\u6570\u636e\u96c6\u4e0a\u7684\u51c6\u786e\u7387\u6c47\u603b\u3002<\/p>\n<h3 id=\"%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E7%AE%A1%E9%81%93%E5%BB%BA%E6%A8%A1%E4%B8%8E%E7%89%B9%E5%BE%81%E6%8F%90%E5%8F%96\">\u673a\u5668\u5b66\u4e60\u7ba1\u9053\u5efa\u6a21\u4e0e\u7279\u5f81\u63d0\u53d6<\/h3>\n<p>\u5728\u673a\u5668\u5b66\u4e60\u6a21\u578b\u7684\u7279\u5f81\u63d0\u53d6\u9636\u6bb5\u4e5f\u53ef\u80fd\u53d1\u751f\u6570\u636e\u6cc4\u9732\u3002\u56e0\u6b64&#xff0c;\u7279\u5f81\u63d0\u53d6\u8fc7\u7a0b\u4e5f\u5e94\u53d7\u5230\u9650\u5236&#xff0c;\u4ee5\u9632\u6b62\u8bad\u7ec3\u6570\u636e\u96c6\u4e2d\u51fa\u73b0\u6570\u636e\u6cc4\u9732\u3002\u4e0e\u6570\u636e\u51c6\u5907\u9636\u6bb5\u7c7b\u4f3c&#xff0c;\u901a\u8fc7\u4f7f\u7528\u673a\u5668\u5b66\u4e60\u7ba1\u9053&#xff0c;\u4e5f\u53ef\u4ee5\u9632\u6b62\u8fd9\u79cd\u6570\u636e\u6cc4\u9732\u3002\u7ba1\u9053\u63d0\u4f9b\u7684\u5de5\u5177 FeatureUnion&#xff08;\u7279\u5f81\u8054\u5408&#xff09;\u53ef\u7528\u4e8e\u6b64\u76ee\u7684\u3002<\/p>\n<h4 id=\"%E7%A4%BA%E4%BE%8B\">\u793a\u4f8b<\/h4>\n<p>\u4ee5\u4e0b\u662f\u4e00\u4e2a Python \u793a\u4f8b&#xff0c;\u5c55\u793a\u4e86\u7279\u5f81\u63d0\u53d6\u548c\u6a21\u578b\u8bc4\u4f30\u7684\u5de5\u4f5c\u6d41\u3002\u672c\u793a\u4f8b\u540c\u6837\u4f7f\u7528 Scikit-learn \u4e2d\u7684\u76ae\u9a6c\u5370\u7b2c\u5b89\u4eba\u7cd6\u5c3f\u75c5\u6570\u636e\u96c6\u3002\u9996\u5148&#xff0c;\u901a\u8fc7\u4e3b\u6210\u5206\u5206\u6790&#xff08;PCA&#xff09;\u63d0\u53d6 3 \u4e2a\u7279\u5f81&#xff1b;\u7136\u540e&#xff0c;\u901a\u8fc7\u7edf\u8ba1\u5206\u6790\u63d0\u53d6 6 \u4e2a\u7279\u5f81&#xff1b;\u7279\u5f81\u63d0\u53d6\u540e&#xff0c;\u4f7f\u7528 FeatureUnion \u5de5\u5177\u7ec4\u5408\u591a\u4e2a\u7279\u5f81\u9009\u62e9\u548c\u63d0\u53d6\u8fc7\u7a0b\u7684\u7ed3\u679c&#xff1b;\u6700\u540e&#xff0c;\u6784\u5efa\u4e00\u4e2a\u903b\u8f91\u56de\u5f52&#xff08;Logistic Regression&#xff09;\u6a21\u578b&#xff0c;\u5e76\u4f7f\u7528 20 \u6298\u4ea4\u53c9\u9a8c\u8bc1\u5bf9\u7ba1\u9053\u8fdb\u884c\u8bc4\u4f30\u3002\u9996\u5148&#xff0c;\u5bfc\u5165\u6240\u9700\u7684\u5e93&#xff1a;<\/p>\n<p>from pandas import read_csv<br \/>\nfrom sklearn.model_selection import KFold<br \/>\nfrom sklearn.model_selection import cross_val_score<br \/>\nfrom sklearn.pipeline import Pipeline<br \/>\nfrom sklearn.pipeline import FeatureUnion<br \/>\nfrom sklearn.linear_model import LogisticRegression<br \/>\nfrom sklearn.decomposition import PCA<br \/>\nfrom sklearn.feature_selection import SelectKBest<\/p>\n<p>\u63a5\u4e0b\u6765&#xff0c;\u6309\u7167\u4e4b\u524d\u7684\u793a\u4f8b\u52a0\u8f7d\u76ae\u9a6c\u5370\u7b2c\u5b89\u4eba\u7cd6\u5c3f\u75c5\u6570\u636e\u96c6&#xff1a;<\/p>\n<p>path &#061; r&#034;C:\\\\pima-indians-diabetes.csv&#034;<br \/>\nheadernames &#061; [&#039;preg&#039;, &#039;plas&#039;, &#039;pres&#039;, &#039;skin&#039;, &#039;test&#039;, &#039;mass&#039;, &#039;pedi&#039;, &#039;age&#039;, &#039;class&#039;]<br \/>\ndata &#061; read_csv(path, names&#061;headernames)<br \/>\narray &#061; data.values<br \/>\nX &#061; array[:, 0:8]<br \/>\nY &#061; array[:, 8]<\/p>\n<p>\u7136\u540e&#xff0c;\u521b\u5efa\u7279\u5f81\u8054\u5408&#xff1a;<\/p>\n<p>features &#061; []<br \/>\nfeatures.append((&#039;pca&#039;, PCA(n_components&#061;3)))<br \/>\nfeatures.append((&#039;select_best&#039;, SelectKBest(k&#061;6)))<br \/>\nfeature_union &#061; FeatureUnion(features)<\/p>\n<p>\u63a5\u4e0b\u6765&#xff0c;\u901a\u8fc7\u4ee5\u4e0b\u4ee3\u7801\u521b\u5efa\u7ba1\u9053&#xff1a;<\/p>\n<p>estimators &#061; []<br \/>\nestimators.append((&#039;feature_union&#039;, feature_union))<br \/>\nestimators.append((&#039;logistic&#039;, LogisticRegression(max_iter&#061;1000)))  # \u589e\u52a0max_iter\u4ee5\u786e\u4fdd\u6a21\u578b\u6536\u655b<br \/>\nmodel &#061; Pipeline(estimators)<\/p>\n<p>\u6700\u540e&#xff0c;\u8bc4\u4f30\u8be5\u7ba1\u9053\u5e76\u8f93\u51fa\u5176\u51c6\u786e\u7387&#xff1a;<\/p>\n<p>kfold &#061; KFold(n_splits&#061;20, random_state&#061;7, shuffle&#061;True)  # \u6dfb\u52a0shuffle&#061;True\u4ee5\u786e\u4fdd\u7ed3\u679c\u53ef\u590d\u73b0&#xff08;Scikit-learn 1.2&#043;\u7248\u672c&#xff09;<br \/>\nresults &#061; cross_val_score(model, X, Y, cv&#061;kfold)<br \/>\nprint(results.mean())<\/p>\n<h5 id=\"%E8%BE%93%E5%87%BA%E7%BB%93%E6%9E%9C\">\u8f93\u51fa\u7ed3\u679c<\/h5>\n<p>plaintext<\/p>\n<p>0.7789811066126855<\/p>\n<p>\u4e0a\u8ff0\u8f93\u51fa\u7ed3\u679c\u662f\u8be5\u8bbe\u7f6e\u5728\u6570\u636e\u96c6\u4e0a\u7684\u51c6\u786e\u7387\u6c47\u603b\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u6458\u8981&#xff1a;\u672c\u6587\u4ecb\u7ecd\u4e86\u673a\u5668\u5b66\u4e60\u7ba1\u9053&#xff08;Pipeline&#xff09;\u7684\u6982\u5ff5\u53ca\u5176\u5728\u6570\u636e\u79d1\u5b66\u5de5\u4f5c\u6d41\u4e2d\u7684\u91cd\u8981\u6027\u3002\u7ba1\u9053\u901a\u8fc7\u6807\u51c6\u5316\u6d41\u7a0b\u5b9e\u73b0\u4ece\u6570\u636e\u6444\u5165\u5230\u6a21\u578b\u90e8\u7f72\u7684\u5168\u8fc7\u7a0b\u81ea\u52a8\u5316&#xff0c;\u5305\u542b\u6570\u636e\u51c6\u5907\u3001\u6a21\u578b\u8bad\u7ec3\u3001\u8bc4\u4f30\u548c\u518d\u8bad\u7ec3\u7b49\u5173\u952e\u73af\u8282\u3002\u6587\u7ae0\u5206\u6790\u4e86\u6570\u636e\u8d28\u91cf\u3001\u53ef\u9760\u6027\u548c\u53ef\u8bbf\u95ee\u6027\u4e09\u5927\u6311\u6218&#xff0c;\u5e76\u901a\u8fc7\u4e24\u4e2aPython\u5b9e\u4f8b&#xff08;\u4f7f\u7528Scikit-learn\u7684\u76ae\u9a6c\u5370\u7b2c\u5b89\u4eba\u7cd6\u5c3f\u75c5\u6570\u636e\u96c6&#xff09;\u6f14\u793a\u4e86\u5982\u4f55\u5229\u7528\u7ba1\u9053\u9632\u6b62\u6570\u636e\u6cc4\u9732&#xff1a;\u7b2c\u4e00\u4e2a\u793a\u4f8b\u5c55\u793a<\/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":[50,207,86],"topic":[],"class_list":["post-62426","post","type-post","status-publish","format-standard","hentry","category-server","tag-50","tag-207","tag-86"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v20.3 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>\u673a\u5668\u5b66\u4e60 - \u81ea\u52a8\u5316\u5de5\u4f5c\u6d41 - \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\/62426.html\" \/>\n<meta property=\"og:locale\" content=\"zh_CN\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"\u673a\u5668\u5b66\u4e60 - \u81ea\u52a8\u5316\u5de5\u4f5c\u6d41 - \u7f51\u7855\u4e92\u8054\u5e2e\u52a9\u4e2d\u5fc3\" \/>\n<meta property=\"og:description\" content=\"\u6458\u8981&#xff1a;\u672c\u6587\u4ecb\u7ecd\u4e86\u673a\u5668\u5b66\u4e60\u7ba1\u9053&#xff08;Pipeline&#xff09;\u7684\u6982\u5ff5\u53ca\u5176\u5728\u6570\u636e\u79d1\u5b66\u5de5\u4f5c\u6d41\u4e2d\u7684\u91cd\u8981\u6027\u3002\u7ba1\u9053\u901a\u8fc7\u6807\u51c6\u5316\u6d41\u7a0b\u5b9e\u73b0\u4ece\u6570\u636e\u6444\u5165\u5230\u6a21\u578b\u90e8\u7f72\u7684\u5168\u8fc7\u7a0b\u81ea\u52a8\u5316&#xff0c;\u5305\u542b\u6570\u636e\u51c6\u5907\u3001\u6a21\u578b\u8bad\u7ec3\u3001\u8bc4\u4f30\u548c\u518d\u8bad\u7ec3\u7b49\u5173\u952e\u73af\u8282\u3002\u6587\u7ae0\u5206\u6790\u4e86\u6570\u636e\u8d28\u91cf\u3001\u53ef\u9760\u6027\u548c\u53ef\u8bbf\u95ee\u6027\u4e09\u5927\u6311\u6218&#xff0c;\u5e76\u901a\u8fc7\u4e24\u4e2aPython\u5b9e\u4f8b&#xff08;\u4f7f\u7528Scikit-learn\u7684\u76ae\u9a6c\u5370\u7b2c\u5b89\u4eba\u7cd6\u5c3f\u75c5\u6570\u636e\u96c6&#xff09;\u6f14\u793a\u4e86\u5982\u4f55\u5229\u7528\u7ba1\u9053\u9632\u6b62\u6570\u636e\u6cc4\u9732&#xff1a;\u7b2c\u4e00\u4e2a\u793a\u4f8b\u5c55\u793a\" 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