{"id":1169,"date":"2023-07-27T10:29:04","date_gmt":"2023-07-27T10:29:04","guid":{"rendered":"https:\/\/statorials.org\/cn\/python%e4%b8%ad%e7%9a%84%e4%ba%8c%e6%ac%a1%e5%88%a4%e5%88%ab%e5%88%86%e6%9e%90\/"},"modified":"2023-07-27T10:29:04","modified_gmt":"2023-07-27T10:29:04","slug":"python%e4%b8%ad%e7%9a%84%e4%ba%8c%e6%ac%a1%e5%88%a4%e5%88%ab%e5%88%86%e6%9e%90","status":"publish","type":"post","link":"https:\/\/statorials.org\/cn\/python%e4%b8%ad%e7%9a%84%e4%ba%8c%e6%ac%a1%e5%88%a4%e5%88%ab%e5%88%86%e6%9e%90\/","title":{"rendered":"Python \u4e2d\u7684\u4e8c\u6b21\u5224\u522b\u5206\u6790\uff08\u4e00\u6b65\u4e00\u6b65\uff09"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\">\u5f53\u60a8\u6709\u4e00\u7ec4\u9884\u6d4b\u53d8\u91cf\u5e76\u4e14\u60f3\u8981\u5c06<a href=\"https:\/\/statorials.org\/cn\/\u53d8\u91cf\u89e3\u91ca\u6027\u53cd\u5e94\/\" target=\"_blank\" rel=\"noopener noreferrer\">\u54cd\u5e94\u53d8\u91cf<\/a>\u5206\u7c7b\u4e3a\u4e24\u4e2a\u6216\u591a\u4e2a\u7c7b\u65f6\uff0c\u53ef\u4ee5\u4f7f\u7528 <a href=\"https:\/\/statorials.org\/cn\/\u4e8c\u6b21\u5224\u522b\u5206\u6790\/\" target=\"_blank\" rel=\"noopener noreferrer\">\u4e8c\u6b21\u5224\u522b\u5206\u6790<\/a>\u65b9\u6cd5\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u5b83\u88ab\u8ba4\u4e3a\u662f<a href=\"https:\/\/statorials.org\/cn\/python\u4e2d\u7684\u7ebf\u6027\u5224\u522b\u5206\u6790\/\" target=\"_blank\" rel=\"noopener noreferrer\">\u7ebf\u6027\u5224\u522b\u5206\u6790<\/a>\u7684\u975e\u7ebf\u6027\u7b49\u4ef7\u7269\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u672c\u6559\u7a0b\u63d0\u4f9b\u4e86\u5982\u4f55\u5728 Python \u4e2d\u6267\u884c\u4e8c\u6b21\u5224\u522b\u5206\u6790\u7684\u5206\u6b65\u793a\u4f8b\u3002<\/span><\/p>\n<h3><span style=\"color: #000000;\"><strong>\u7b2c 1 \u6b65\uff1a\u52a0\u8f7d\u5fc5\u8981\u7684\u5e93<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u9996\u5148\uff0c\u6211\u4eec\u5c06\u52a0\u8f7d\u6b64\u793a\u4f8b\u6240\u9700\u7684\u51fd\u6570\u548c\u5e93\uff1a<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <span style=\"color: #000000;\"><b><span style=\"color: #008000;\">from<\/span> sklearn. <span style=\"color: #3366ff;\">model_selection<\/span> <span style=\"color: #008000;\">import<\/span> train_test_split\n<span style=\"color: #008000;\">from<\/span> sklearn. <span style=\"color: #3366ff;\">model_selection<\/span> <span style=\"color: #008000;\">import<\/span> RepeatedStratifiedKFold\n<span style=\"color: #008000;\">from<\/span> sklearn. <span style=\"color: #3366ff;\">model_selection<\/span> <span style=\"color: #008000;\">import<\/span> cross_val_score\n<span style=\"color: #008000;\">from<\/span> sklearn. <span style=\"color: #3366ff;\">discriminant_analysis<\/span> <span style=\"color: #008000;\">import<\/span> QuadraticDiscriminantAnalysis \n<span style=\"color: #008000;\">from<\/span> sklearn <span style=\"color: #008000;\">import<\/span> datasets\n<span style=\"color: #008000;\">import<\/span> matplotlib. <span style=\"color: #3366ff;\">pyplot<\/span> <span style=\"color: #008000;\">as<\/span> plt\n<span style=\"color: #008000;\">import<\/span> pandas <span style=\"color: #008000;\">as<\/span> pd\n<span style=\"color: #008000;\">import<\/span> numpy <span style=\"color: #008000;\">as<\/span> np<\/b><\/span><\/pre>\n<h3><span style=\"color: #000000;\"><strong>\u7b2c2\u6b65\uff1a\u52a0\u8f7d\u6570\u636e<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u5bf9\u4e8e\u6b64\u793a\u4f8b\uff0c\u6211\u4eec\u5c06\u4f7f\u7528 sklearn \u5e93\u4e2d\u7684<strong>iris<\/strong>\u6570\u636e\u96c6\u3002\u4ee5\u4e0b\u4ee3\u7801\u5c55\u793a\u4e86\u5982\u4f55\u52a0\u8f7d\u6b64\u6570\u636e\u96c6\u5e76\u5c06\u5176\u8f6c\u6362\u4e3a pandas DataFrame \u4ee5\u65b9\u4fbf\u4f7f\u7528\uff1a<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#load <em>iris<\/em> dataset<\/span>\niris = datasets. <span style=\"color: #3366ff;\">load_iris<\/span> ()\n\n<span style=\"color: #008080;\">#convert dataset to pandas DataFrame\n<\/span>df = pd.DataFrame(data = np.c_[iris[' <span style=\"color: #008000;\">data<\/span> '], iris[' <span style=\"color: #008000;\">target<\/span> ']],\n                 columns = iris[' <span style=\"color: #008000;\">feature_names<\/span> '] + [' <span style=\"color: #008000;\">target<\/span> '])\ndf[' <span style=\"color: #008000;\">species<\/span> '] = pd. <span style=\"color: #3366ff;\">Categorical<\/span> . <span style=\"color: #3366ff;\">from_codes<\/span> (iris.target, iris.target_names)\ndf.columns = [' <span style=\"color: #008000;\">s_length<\/span> ', ' <span style=\"color: #008000;\">s_width<\/span> ', ' <span style=\"color: #008000;\">p_length<\/span> ', ' <span style=\"color: #008000;\">p_width<\/span> ', ' <span style=\"color: #008000;\">target<\/span> ', ' <span style=\"color: #008000;\">species<\/span> ']\n\n<span style=\"color: #008080;\">#view first six rows of DataFrame\n<\/span>df. <span style=\"color: #3366ff;\">head<\/span> ()\n\n   s_length s_width p_length p_width target species\n0 5.1 3.5 1.4 0.2 0.0 setosa\n1 4.9 3.0 1.4 0.2 0.0 setosa\n2 4.7 3.2 1.3 0.2 0.0 setosa\n3 4.6 3.1 1.5 0.2 0.0 setosa\n4 5.0 3.6 1.4 0.2 0.0 setosa\n\n<span style=\"color: #3366ff;\"><span style=\"color: #008080;\">#find how many total observations are in dataset<\/span>\n<span style=\"color: #000000;\">len(df.index)\n\n150<\/span><\/span><\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u6211\u4eec\u53ef\u4ee5\u770b\u5230\u6570\u636e\u96c6\u603b\u5171\u5305\u542b 150 \u4e2a\u89c2\u6d4b\u503c\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u5bf9\u4e8e\u8fd9\u4e2a\u4f8b\u5b50\uff0c\u6211\u4eec\u5c06\u6784\u5efa\u4e00\u4e2a\u4e8c\u6b21\u5224\u522b\u5206\u6790\u6a21\u578b\u6765\u5bf9\u7ed9\u5b9a\u82b1\u6735\u6240\u5c5e\u7684\u7269\u79cd\u8fdb\u884c\u5206\u7c7b\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u6211\u4eec\u5c06\u5728\u6a21\u578b\u4e2d\u4f7f\u7528\u4ee5\u4e0b\u9884\u6d4b\u53d8\u91cf\uff1a<\/span><\/p>\n<ul>\n<li><span style=\"color: #000000;\">\u843c\u7247\u957f\u5ea6<\/span><\/li>\n<li><span style=\"color: #000000;\">\u843c\u7247\u5bbd\u5ea6<\/span><\/li>\n<li><span style=\"color: #000000;\">\u82b1\u74e3\u957f\u5ea6<\/span><\/li>\n<li><span style=\"color: #000000;\">\u82b1\u74e3\u5bbd\u5ea6<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u6211\u4eec\u5c06\u4f7f\u7528\u5b83\u4eec\u6765\u9884\u6d4b<em>\u7269\u79cd<\/em>\u54cd\u5e94\u53d8\u91cf\uff0c\u8be5\u53d8\u91cf\u652f\u6301\u4ee5\u4e0b\u4e09\u4e2a\u6f5c\u5728\u7c7b\u522b\uff1a<\/span><\/p>\n<ul>\n<li><span style=\"color: #000000;\">\u5c71\u6bdb\u6989<\/span><\/li>\n<li><span style=\"color: #000000;\">\u6742\u8272<\/span><\/li>\n<li><span style=\"color: #000000;\">\u5f17\u5409\u5c3c\u4e9a\u5dde<\/span><\/li>\n<\/ul>\n<h3><span style=\"color: #000000;\"><strong>\u6b65\u9aa43\uff1a\u8c03\u6574QDA\u6a21\u578b<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u5c06\u4f7f\u7528 sklearn \u7684<a href=\"https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.html\" target=\"_blank\" rel=\"noopener noreferrer\">QuadraticDiscriminantAnalsys<\/a>\u51fd\u6570\u5c06 QDA \u6a21\u578b\u62df\u5408\u5230\u6211\u4eec\u7684\u6570\u636e\uff1a<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#define predictor and response variables\n<\/span>X = df[[' <span style=\"color: #008000;\">s_length<\/span> ',' <span style=\"color: #008000;\">s_width<\/span> ',' <span style=\"color: #008000;\">p_length<\/span> ',' <span style=\"color: #008000;\">p_width<\/span> ']]\ny = df[' <span style=\"color: #008000;\">species<\/span> ']\n\n<span style=\"color: #008080;\">#Fit the QDA model\n<\/span>model = QuadraticDiscriminantAnalysis()\nmodel. <span style=\"color: #3366ff;\">fit<\/span> (x,y)\n<\/strong><\/pre>\n<h3><span style=\"color: #000000;\"><strong>\u7b2c 4 \u6b65\uff1a\u4f7f\u7528\u6a21\u578b\u8fdb\u884c\u9884\u6d4b<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u4e00\u65e6\u6211\u4eec\u4f7f\u7528\u6570\u636e\u62df\u5408\u4e86\u6a21\u578b\uff0c\u6211\u4eec\u5c31\u53ef\u4ee5\u4f7f\u7528\u91cd\u590d\u5206\u5c42 k \u500d\u4ea4\u53c9\u9a8c\u8bc1\u6765\u8bc4\u4f30\u6a21\u578b\u7684\u6027\u80fd\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u5728\u6b64\u793a\u4f8b\u4e2d\uff0c\u6211\u4eec\u5c06\u4f7f\u7528 10 \u6b21\u6298\u53e0\u548c 3 \u6b21\u91cd\u590d\uff1a<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#Define method to evaluate model\n<span style=\"color: #000000;\">cv = RepeatedStratifiedKFold(n_splits= <span style=\"color: #008000;\">10<\/span> , n_repeats= <span style=\"color: #008000;\">3<\/span> , random_state= <span style=\"color: #008000;\">1<\/span> )\n<\/span>\n#evaluate model\n<span style=\"color: #000000;\">scores = cross_val_score(model, X, y, scoring=' <span style=\"color: #008000;\">accuracy<\/span> ', cv=cv, n_jobs=-1)\nprint( <span style=\"color: #3366ff;\">np.mean<\/span> (scores))<\/span>  \n\n<span style=\"color: #000000;\">0.97333333333334<\/span><\/span><\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u6211\u4eec\u53ef\u4ee5\u770b\u5230\u8be5\u6a21\u578b\u7684\u5e73\u5747\u51c6\u786e\u7387\u8fbe\u5230\u4e86<strong>97.33%<\/strong> \u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u6211\u4eec\u8fd8\u53ef\u4ee5\u4f7f\u7528\u8be5\u6a21\u578b\u6839\u636e\u8f93\u5165\u503c\u6765\u9884\u6d4b\u65b0\u82b1\u5c5e\u4e8e\u54ea\u4e2a\u7c7b\uff1a<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#define new observation\n<\/span>new = [5, 3, 1, .4]\n\n<span style=\"color: #008080;\">#predict which class the new observation belongs to\n<\/span>model. <span style=\"color: #3366ff;\">predict<\/span> ([new])\n\narray(['setosa'], dtype='&lt;U10')\n<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u6211\u4eec\u770b\u5230\u6a21\u578b\u9884\u6d4b\u8fd9\u4e2a\u65b0\u89c2\u5bdf\u7ed3\u679c\u5c5e\u4e8e\u79f0\u4e3a<em>setosa<\/em>\u7684\u7269\u79cd\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u60a8\u53ef\u4ee5<a href=\"https:\/\/github.com\/Statorials\/Python-Guides\/blob\/main\/quadratic_discriminant_analysis.py\" target=\"_blank\" rel=\"noopener noreferrer\">\u5728\u6b64\u5904<\/a>\u627e\u5230\u672c\u6559\u7a0b\u4e2d\u4f7f\u7528\u7684\u5b8c\u6574 Python \u4ee3\u7801\u3002<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u5f53\u60a8\u6709\u4e00\u7ec4\u9884\u6d4b\u53d8\u91cf\u5e76\u4e14\u60f3\u8981\u5c06\u54cd\u5e94\u53d8\u91cf\u5206\u7c7b\u4e3a\u4e24\u4e2a\u6216\u591a\u4e2a\u7c7b\u65f6\uff0c\u53ef\u4ee5\u4f7f\u7528 \u4e8c\u6b21\u5224\u522b\u5206\u6790\u65b9\u6cd5\u3002 \u5b83\u88ab\u8ba4\u4e3a\u662f\u7ebf\u6027\u5224\u522b\u5206\u6790 [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[11],"tags":[],"class_list":["post-1169","post","type-post","status-publish","format-standard","hentry","category-11"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v21.5 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Python 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