{"id":950357,"date":"2024-12-27T00:33:12","date_gmt":"2024-12-26T16:33:12","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/950357.html"},"modified":"2024-12-27T00:33:14","modified_gmt":"2024-12-26T16:33:14","slug":"python%e5%a6%82%e4%bd%95%e8%bf%90%e7%94%a8svm","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/950357.html","title":{"rendered":"python\u5982\u4f55\u8fd0\u7528svm"},"content":{"rendered":"<p style=\"text-align:center;\" ><img decoding=\"async\" src=\"https:\/\/cdn-kb.worktile.com\/kb\/wp-content\/uploads\/2024\/04\/25085015\/70f7432a-18e2-4f7f-9dfd-8e4ed91ffc89.webp\" alt=\"python\u5982\u4f55\u8fd0\u7528svm\" \/><\/p>\n<p><p> <strong>\u5728Python\u4e2d\u4f7f\u7528SVM\uff08\u652f\u6301\u5411\u91cf\u673a\uff09\u7684\u6b65\u9aa4\u4e3b\u8981\u5305\u62ec\uff1a\u9009\u62e9\u5408\u9002\u7684\u5e93\uff08\u5982scikit-learn\uff09\u3001\u5bfc\u5165\u6570\u636e\u3001\u6570\u636e\u9884\u5904\u7406\u3001\u9009\u62e9\u5185\u6838\u3001\u8bad\u7ec3\u6a21\u578b\u548c\u8bc4\u4f30\u6a21\u578b\u3002SVM\u662f\u4e00\u79cd\u7528\u4e8e\u5206\u7c7b\u548c\u56de\u5f52\u7684\u5f3a\u5927\u76d1\u7763\u5b66\u4e60\u7b97\u6cd5\uff0c\u5176\u4e2d\u9009\u62e9\u5408\u9002\u7684\u5185\u6838\u51fd\u6570\u662f\u5173\u952e\uff0c\u56e0\u4e3a\u5b83\u76f4\u63a5\u5f71\u54cd\u6a21\u578b\u7684\u6027\u80fd\u3002<\/strong>\u5728\u6b64\uff0c\u6211\u4eec\u5c06\u6df1\u5165\u63a2\u8ba8\u5982\u4f55\u5728Python\u4e2d\u6709\u6548\u5730\u5e94\u7528SVM\uff0c\u5305\u62ec\u5982\u4f55\u9009\u62e9\u5185\u6838\u51fd\u6570\u4ee5\u53ca\u5982\u4f55\u8bc4\u4f30\u6a21\u578b\u7684\u6027\u80fd\u3002<\/p>\n<\/p>\n<p><p>\u4e00\u3001\u9009\u62e9\u5408\u9002\u7684\u5e93<\/p>\n<\/p>\n<p><p>Python\u4e2d\u6709\u591a\u4e2a<a href=\"https:\/\/docs.pingcode.com\/ask\/59192.html\" target=\"_blank\">\u673a\u5668\u5b66\u4e60<\/a>\u5e93\u53ef\u4ee5\u7528\u6765\u5b9e\u73b0SVM\uff0c\u4f46\u6700\u6d41\u884c\u548c\u6613\u4e8e\u4f7f\u7528\u7684\u5e93\u662fscikit-learn\u3002scikit-learn\u63d0\u4f9b\u4e86\u7b80\u5355\u4e14\u5f3a\u5927\u7684\u63a5\u53e3\u6765\u5b9e\u73b0\u5404\u79cd\u673a\u5668\u5b66\u4e60\u7b97\u6cd5\uff0c\u5305\u62ecSVM\u3002<\/p>\n<\/p>\n<ol>\n<li>\n<p><strong>\u5b89\u88c5scikit-learn<\/strong><\/p>\n<p>\u8981\u4f7f\u7528scikit-learn\uff0c\u9996\u5148\u9700\u8981\u786e\u4fdd\u5df2\u5b89\u88c5\u8be5\u5e93\u3002\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u547d\u4ee4\u5b89\u88c5\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-bash\">pip install scikit-learn<\/p>\n<p><\/code><\/pre>\n<\/p>\n<\/li>\n<li>\n<p><strong>\u5bfc\u5165scikit-learn\u6a21\u5757<\/strong><\/p>\n<p>\u5728\u5f00\u59cb\u4f7f\u7528SVM\u4e4b\u524d\uff0c\u9700\u8981\u5bfc\u5165\u76f8\u5173\u6a21\u5757\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">from sklearn import datasets<\/p>\n<p>from sklearn.model_selection import tr<a href=\"https:\/\/docs.pingcode.com\/blog\/59162.html\" target=\"_blank\">AI<\/a>n_test_split<\/p>\n<p>from sklearn.preprocessing import StandardScaler<\/p>\n<p>from sklearn.svm import SVC<\/p>\n<p>from sklearn.metrics import classification_report, confusion_matrix<\/p>\n<p><\/code><\/pre>\n<\/p>\n<\/li>\n<\/ol>\n<p><p>\u4e8c\u3001\u5bfc\u5165\u548c\u51c6\u5907\u6570\u636e<\/p>\n<\/p>\n<p><p>\u6570\u636e\u662f\u673a\u5668\u5b66\u4e60\u7684\u57fa\u7840\uff0c\u9009\u62e9\u548c\u51c6\u5907\u6570\u636e\u662f\u6784\u5efa\u53ef\u9760\u6a21\u578b\u7684\u7b2c\u4e00\u6b65\u3002<\/p>\n<\/p>\n<ol>\n<li>\n<p><strong>\u5bfc\u5165\u6570\u636e\u96c6<\/strong><\/p>\n<p>scikit-learn\u63d0\u4f9b\u4e86\u591a\u79cd\u5185\u7f6e\u6570\u636e\u96c6\uff0c\u5982\u9e22\u5c3e\u82b1\u6570\u636e\u96c6\uff0c\u53ef\u4ee5\u7528\u4e8e\u793a\u4f8b\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">iris = datasets.load_iris()<\/p>\n<p>X = iris.data<\/p>\n<p>y = iris.target<\/p>\n<p><\/code><\/pre>\n<\/p>\n<\/li>\n<li>\n<p><strong>\u6570\u636e\u5212\u5206<\/strong><\/p>\n<p>\u5c06\u6570\u636e\u96c6\u5212\u5206\u4e3a\u8bad\u7ec3\u96c6\u548c\u6d4b\u8bd5\u96c6\uff0c\u4ee5\u4fbf\u6a21\u578b\u53ef\u4ee5\u5728\u8bad\u7ec3\u6570\u636e\u4e0a\u5b66\u4e60\u5e76\u5728\u6d4b\u8bd5\u6570\u636e\u4e0a\u8fdb\u884c\u9a8c\u8bc1\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<\/li>\n<li>\n<p><strong>\u6570\u636e\u9884\u5904\u7406<\/strong><\/p>\n<p>\u6570\u636e\u9884\u5904\u7406\u662f\u63d0\u9ad8\u6a21\u578b\u6027\u80fd\u7684\u91cd\u8981\u6b65\u9aa4\u3002\u6807\u51c6\u5316\u662f\u5e38\u89c1\u7684\u9884\u5904\u7406\u65b9\u6cd5\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">scaler = StandardScaler()<\/p>\n<p>X_train = scaler.fit_transform(X_train)<\/p>\n<p>X_test = scaler.transform(X_test)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<\/li>\n<\/ol>\n<p><p>\u4e09\u3001\u9009\u62e9\u5185\u6838\u51fd\u6570<\/p>\n<\/p>\n<p><p>SVM\u7684\u6838\u5fc3\u662f\u9009\u62e9\u5408\u9002\u7684\u5185\u6838\u51fd\u6570\u3002\u5e38\u7528\u7684\u5185\u6838\u51fd\u6570\u6709\u7ebf\u6027\u5185\u6838\u3001\u591a\u9879\u5f0f\u5185\u6838\u548cRBF\u5185\u6838\u7b49\u3002<\/p>\n<\/p>\n<ol>\n<li>\n<p><strong>\u7ebf\u6027\u5185\u6838<\/strong><\/p>\n<p>\u7ebf\u6027\u5185\u6838\u9002\u7528\u4e8e\u7ebf\u6027\u53ef\u5206\u7684\u6570\u636e\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">linear_svc = SVC(kernel=&#39;linear&#39;)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<\/li>\n<li>\n<p><strong>\u591a\u9879\u5f0f\u5185\u6838<\/strong><\/p>\n<p>\u591a\u9879\u5f0f\u5185\u6838\u9002\u7528\u4e8e\u590d\u6742\u7684\u591a\u9879\u5f0f\u5206\u5e03\u7684\u6570\u636e\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">poly_svc = SVC(kernel=&#39;poly&#39;, degree=3)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<\/li>\n<li>\n<p><strong>RBF\u5185\u6838<\/strong><\/p>\n<p>RBF\u5185\u6838\u662f\u6700\u6d41\u884c\u7684\u975e\u7ebf\u6027\u5185\u6838\uff0c\u9002\u7528\u4e8e\u5927\u591a\u6570\u60c5\u51b5\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">rbf_svc = SVC(kernel=&#39;rbf&#39;, gamma=&#39;scale&#39;)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<\/li>\n<\/ol>\n<p><p>\u56db\u3001\u8bad\u7ec3\u548c\u8bc4\u4f30\u6a21\u578b<\/p>\n<\/p>\n<p><p>\u4e00\u65e6\u9009\u62e9\u4e86\u5408\u9002\u7684\u5185\u6838\u51fd\u6570\uff0c\u5c31\u53ef\u4ee5\u8bad\u7ec3\u6a21\u578b\u5e76\u8fdb\u884c\u8bc4\u4f30\u3002<\/p>\n<\/p>\n<ol>\n<li>\n<p><strong>\u8bad\u7ec3\u6a21\u578b<\/strong><\/p>\n<p>\u4f7f\u7528\u8bad\u7ec3\u6570\u636e\u6765\u62df\u5408\u6a21\u578b\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">rbf_svc.fit(X_train, y_train)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<\/li>\n<li>\n<p><strong>\u8bc4\u4f30\u6a21\u578b<\/strong><\/p>\n<p>\u4f7f\u7528\u6d4b\u8bd5\u6570\u636e\u8bc4\u4f30\u6a21\u578b\u7684\u6027\u80fd\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">y_pred = rbf_svc.predict(X_test)<\/p>\n<p>print(confusion_matrix(y_test, y_pred))<\/p>\n<p>print(classification_report(y_test, y_pred))<\/p>\n<p><\/code><\/pre>\n<\/p>\n<\/li>\n<li>\n<p><strong>\u4ea4\u53c9\u9a8c\u8bc1<\/strong><\/p>\n<p>\u4ea4\u53c9\u9a8c\u8bc1\u662f\u4e00\u79cd\u66f4\u53ef\u9760\u7684\u8bc4\u4f30\u65b9\u6cd5\uff0c\u53ef\u4ee5\u901a\u8fc7scikit-learn\u4e2d\u7684<code>cross_val_score<\/code>\u5b9e\u73b0\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">from sklearn.model_selection import cross_val_score<\/p>\n<p>scores = cross_val_score(rbf_svc, X, y, cv=5)<\/p>\n<p>print(scores.mean())<\/p>\n<p><\/code><\/pre>\n<\/p>\n<\/li>\n<\/ol>\n<p><p>\u4e94\u3001\u53c2\u6570\u8c03\u4f18<\/p>\n<\/p>\n<p><p>\u4e3a\u4e86\u8fdb\u4e00\u6b65\u63d0\u9ad8\u6a21\u578b\u7684\u6027\u80fd\uff0c\u53ef\u4ee5\u901a\u8fc7\u7f51\u683c\u641c\u7d22\u6765\u8c03\u4f18\u53c2\u6570\u3002<\/p>\n<\/p>\n<ol>\n<li>\n<p><strong>\u7f51\u683c\u641c\u7d22<\/strong><\/p>\n<p>\u4f7f\u7528<code>GridSearchCV<\/code>\u6765\u5bfb\u627e\u6700\u4f18\u53c2\u6570\u7ec4\u5408\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">from sklearn.model_selection import GridSearchCV<\/p>\n<p>param_grid = {<\/p>\n<p>    &#39;C&#39;: [0.1, 1, 10, 100],<\/p>\n<p>    &#39;gamma&#39;: [1, 0.1, 0.01, 0.001],<\/p>\n<p>    &#39;kernel&#39;: [&#39;rbf&#39;, &#39;poly&#39;, &#39;linear&#39;]<\/p>\n<p>}<\/p>\n<p>grid = GridSearchCV(SVC(), param_grid, refit=True, verbose=2)<\/p>\n<p>grid.fit(X_train, y_train)<\/p>\n<p>print(grid.best_params_)<\/p>\n<p>print(grid.best_estimator_)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<\/li>\n<li>\n<p><strong>\u5206\u6790\u7ed3\u679c<\/strong><\/p>\n<p>\u6839\u636e\u7f51\u683c\u641c\u7d22\u7684\u7ed3\u679c\u8c03\u6574\u6a21\u578b\u53c2\u6570\uff0c\u5e76\u91cd\u65b0\u8bc4\u4f30\u6a21\u578b\u6027\u80fd\u3002<\/p>\n<\/p>\n<\/li>\n<\/ol>\n<p><p>\u901a\u8fc7\u4ee5\u4e0a\u6b65\u9aa4\uff0c\u53ef\u4ee5\u5728Python\u4e2d\u9ad8\u6548\u5730\u5e94\u7528SVM\u8fdb\u884c\u5206\u7c7b\u548c\u56de\u5f52\u4efb\u52a1\u3002\u9009\u62e9\u5408\u9002\u7684\u5185\u6838\u51fd\u6570\u548c\u53c2\u6570\u8c03\u4f18\u662f\u5173\u952e\uff0c\u53ef\u4ee5\u663e\u8457\u63d0\u9ad8\u6a21\u578b\u7684\u51c6\u786e\u6027\u548c\u6cdb\u5316\u80fd\u529b\u3002<\/p>\n<\/p>\n<h2><strong>\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n<p> <strong>\u5982\u4f55\u5728Python\u4e2d\u5b89\u88c5SVM\u76f8\u5173\u5e93\uff1f<\/strong><br \/>\u5728Python\u4e2d\u4f7f\u7528\u652f\u6301\u5411\u91cf\u673a(SVM)\u901a\u5e38\u9700\u8981\u5b89\u88c5scikit-learn\u5e93\u3002\u60a8\u53ef\u4ee5\u901a\u8fc7\u8fd0\u884c<code>pip install scikit-learn<\/code>\u547d\u4ee4\u6765\u5b89\u88c5\u5b83\u3002\u6b64\u5916\uff0c\u5982\u679c\u9700\u8981\u8fdb\u884c\u6570\u636e\u5904\u7406\uff0c\u53ef\u4ee5\u8003\u8651\u5b89\u88c5NumPy\u548cPandas\u5e93\uff0c\u547d\u4ee4\u4e3a<code>pip install numpy pandas<\/code>\u3002\u786e\u4fdd\u5728\u5b89\u88c5\u4e4b\u524d\uff0c\u60a8\u7684Python\u73af\u5883\u5df2\u7ecf\u6b63\u786e\u8bbe\u7f6e\u3002<\/p>\n<p><strong>\u4f7f\u7528SVM\u8fdb\u884c\u5206\u7c7b\u4efb\u52a1\u7684\u57fa\u672c\u6b65\u9aa4\u662f\u4ec0\u4e48\uff1f<\/strong><br \/>\u8fdb\u884c\u5206\u7c7b\u4efb\u52a1\u65f6\uff0c\u9996\u5148\u9700\u8981\u51c6\u5907\u6570\u636e\u96c6\u5e76\u8fdb\u884c\u9884\u5904\u7406\uff0c\u5305\u62ec\u6570\u636e\u6e05\u6d17\u548c\u7279\u5f81\u9009\u62e9\u3002\u63a5\u7740\uff0c\u5c06\u6570\u636e\u96c6\u5206\u4e3a\u8bad\u7ec3\u96c6\u548c\u6d4b\u8bd5\u96c6\u3002\u4f7f\u7528<code>SVC<\/code>\u7c7b\u521b\u5efaSVM\u6a21\u578b\uff0c\u5e76\u901a\u8fc7\u8bad\u7ec3\u96c6\u8fdb\u884c\u6a21\u578b\u8bad\u7ec3\u3002\u8bad\u7ec3\u5b8c\u6210\u540e\uff0c\u53ef\u4ee5\u4f7f\u7528\u6d4b\u8bd5\u96c6\u5bf9\u6a21\u578b\u8fdb\u884c\u8bc4\u4f30\uff0c\u67e5\u770b\u5176\u51c6\u786e\u7387\u3001\u53ec\u56de\u7387\u7b49\u6027\u80fd\u6307\u6807\u3002<\/p>\n<p><strong>\u5982\u4f55\u9009\u62e9SVM\u7684\u53c2\u6570\u4ee5\u63d0\u9ad8\u6a21\u578b\u6027\u80fd\uff1f<\/strong><br \/>\u9009\u62e9SVM\u53c2\u6570\u65f6\uff0c\u91cd\u8981\u7684\u8d85\u53c2\u6570\u5305\u62ecC\uff08\u60e9\u7f5a\u53c2\u6570\uff09\u3001kernel\uff08\u6838\u51fd\u6570\uff09\u548cgamma\uff08\u6838\u7cfb\u6570\uff09\u3002\u53ef\u4ee5\u4f7f\u7528\u4ea4\u53c9\u9a8c\u8bc1\u548c\u7f51\u683c\u641c\u7d22\u65b9\u6cd5\u6765\u627e\u5230\u6700\u4f73\u53c2\u6570\u7ec4\u5408\u3002scikit-learn\u63d0\u4f9b\u4e86<code>GridSearchCV<\/code>\u7c7b\uff0c\u53ef\u4ee5\u5e2e\u52a9\u60a8\u7cfb\u7edf\u5730\u641c\u7d22\u6700\u4f73\u53c2\u6570\u914d\u7f6e\uff0c\u5e76\u63d0\u9ad8\u6a21\u578b\u6027\u80fd\u3002\u901a\u8fc7\u5bf9\u6bd4\u4e0d\u540c\u53c2\u6570\u4e0b\u7684\u6a21\u578b\u8868\u73b0\uff0c\u60a8\u53ef\u4ee5\u9009\u62e9\u6700\u4f18\u7684\u53c2\u6570\u7ec4\u5408\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u5728Python\u4e2d\u4f7f\u7528SVM\uff08\u652f\u6301\u5411\u91cf\u673a\uff09\u7684\u6b65\u9aa4\u4e3b\u8981\u5305\u62ec\uff1a\u9009\u62e9\u5408\u9002\u7684\u5e93\uff08\u5982scikit-learn\uff09\u3001\u5bfc\u5165\u6570\u636e\u3001 [&hellip;]","protected":false},"author":3,"featured_media":950360,"comment_status":"closed","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[37],"tags":[],"acf":[],"_links":{"self":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/950357"}],"collection":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/comments?post=950357"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/950357\/revisions"}],"predecessor-version":[{"id":950361,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/950357\/revisions\/950361"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/950360"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=950357"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=950357"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=950357"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}