{"id":978707,"date":"2024-12-27T06:40:00","date_gmt":"2024-12-26T22:40:00","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/978707.html"},"modified":"2024-12-27T06:40:02","modified_gmt":"2024-12-26T22:40:02","slug":"%e5%a6%82%e4%bd%95%e7%94%a8python%e8%ae%ad%e7%bb%83%e6%a8%a1%e5%9e%8b","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/978707.html","title":{"rendered":"\u5982\u4f55\u7528python\u8bad\u7ec3\u6a21\u578b"},"content":{"rendered":"<p style=\"text-align:center;\" ><img decoding=\"async\" src=\"https:\/\/cdn-kb.worktile.com\/kb\/wp-content\/uploads\/2024\/04\/24203554\/d556b00d-cf31-4e8b-a5fa-3601ac5a9a99.webp\" alt=\"\u5982\u4f55\u7528python\u8bad\u7ec3\u6a21\u578b\" \/><\/p>\n<p><p> <strong>\u4f7f\u7528Python\u8bad\u7ec3\u6a21\u578b\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u51e0\u4e2a\u6b65\u9aa4\u5b9e\u73b0\uff1a\u5bfc\u5165\u76f8\u5173\u5e93\u3001\u51c6\u5907\u6570\u636e\u3001\u9009\u62e9\u6a21\u578b\u3001\u8bad\u7ec3\u6a21\u578b\u3001\u8bc4\u4f30\u6a21\u578b\u6027\u80fd\u3001\u4f18\u5316\u6a21\u578b\u3001\u4fdd\u5b58\u6a21\u578b\u3002<\/strong>\u5176\u4e2d\uff0c\u9009\u62e9\u6a21\u578b\u975e\u5e38\u5173\u952e\uff0c\u9700\u8981\u6839\u636e\u5177\u4f53\u95ee\u9898\u9009\u62e9\u5408\u9002\u7684\u7b97\u6cd5\uff0c\u4f8b\u5982\u7ebf\u6027\u56de\u5f52\u7528\u4e8e\u56de\u5f52\u95ee\u9898\uff0c\u903b\u8f91\u56de\u5f52\u7528\u4e8e\u5206\u7c7b\u95ee\u9898\u3002\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u5c06\u8be6\u7ec6\u63a2\u8ba8\u6bcf\u4e00\u4e2a\u6b65\u9aa4\u3002<\/p>\n<\/p>\n<p><p>\u4e00\u3001\u5bfc\u5165\u76f8\u5173\u5e93<\/p>\n<\/p>\n<p><p>\u5728Python\u4e2d\uff0c<a href=\"https:\/\/docs.pingcode.com\/ask\/59192.html\" target=\"_blank\">\u673a\u5668\u5b66\u4e60<\/a>\u7684\u5b9e\u73b0\u4e3b\u8981\u4f9d\u8d56\u4e8e\u4e00\u4e9b\u5f3a\u5927\u7684\u5e93\uff0c\u5982NumPy\u3001Pandas\u3001scikit-learn\u548cTensorFlow\u7b49\u3002\u9996\u5148\uff0c\u6211\u4eec\u9700\u8981\u5bfc\u5165\u8fd9\u4e9b\u5e93\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import numpy as np<\/p>\n<p>import pandas as pd<\/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.metrics import accuracy_score<\/p>\n<p>from sklearn.linear_model import LogisticRegression<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>NumPy\u7528\u4e8e\u5904\u7406\u6570\u503c\u6570\u636e\uff0cPandas\u7528\u4e8e\u6570\u636e\u64cd\u4f5c\uff0cscikit-learn\u662f\u4e00\u4e2a\u529f\u80fd\u5f3a\u5927\u7684\u673a\u5668\u5b66\u4e60\u5e93\uff0c\u5305\u542b\u4e86\u8bb8\u591a\u5e38\u7528\u7684\u7b97\u6cd5\u548c\u5de5\u5177\u3002<\/p>\n<\/p>\n<p><p>\u4e8c\u3001\u51c6\u5907\u6570\u636e<\/p>\n<\/p>\n<p><p>\u6570\u636e\u662f\u8bad\u7ec3\u6a21\u578b\u7684\u57fa\u7840\u3002\u51c6\u5907\u6570\u636e\u7684\u8fc7\u7a0b\u5305\u62ec\u6570\u636e\u6536\u96c6\u3001\u6570\u636e\u6e05\u6d17\u548c\u6570\u636e\u9884\u5904\u7406\u3002<\/p>\n<\/p>\n<ol>\n<li><strong>\u6570\u636e\u6536\u96c6<\/strong>\uff1a\u53ef\u4ee5\u901a\u8fc7CSV\u6587\u4ef6\u3001\u6570\u636e\u5e93\u6216API\u7b49\u65b9\u5f0f\u83b7\u53d6\u6570\u636e\u3002<\/li>\n<li><strong>\u6570\u636e\u6e05\u6d17<\/strong>\uff1a\u5904\u7406\u7f3a\u5931\u503c\u3001\u5f02\u5e38\u503c\u7b49\uff0c\u786e\u4fdd\u6570\u636e\u7684\u8d28\u91cf\u3002<\/li>\n<li><strong>\u6570\u636e\u9884\u5904\u7406<\/strong>\uff1a\u6807\u51c6\u5316\u3001\u5f52\u4e00\u5316\u7b49\u64cd\u4f5c\uff0c\u4f7f\u6570\u636e\u66f4\u9002\u5408\u6a21\u578b\u8bad\u7ec3\u3002<\/li>\n<\/ol>\n<p><p>\u4f8b\u5982\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528Pandas\u8bfb\u53d6CSV\u6587\u4ef6\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">data = pd.read_csv(&#39;data.csv&#39;)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u7136\u540e\u8fdb\u884c\u6570\u636e\u6e05\u6d17\u548c\u9884\u5904\u7406\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">data.dropna(inplace=True)  # \u53bb\u9664\u7f3a\u5931\u503c<\/p>\n<p>data[&#39;feature&#39;] = (data[&#39;feature&#39;] - data[&#39;feature&#39;].mean()) \/ data[&#39;feature&#39;].std()  # \u6807\u51c6\u5316<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u4e09\u3001\u9009\u62e9\u6a21\u578b<\/p>\n<\/p>\n<p><p>\u9009\u62e9\u5408\u9002\u7684\u6a21\u578b\u662f\u81f3\u5173\u91cd\u8981\u7684\u3002\u4e0d\u540c\u7684\u95ee\u9898\u9700\u8981\u4e0d\u540c\u7684\u7b97\u6cd5\u3002\u4ee5\u4e0b\u662f\u4e00\u4e9b\u5e38\u7528\u7684\u673a\u5668\u5b66\u4e60\u7b97\u6cd5\uff1a<\/p>\n<\/p>\n<ul>\n<li><strong>\u7ebf\u6027\u56de\u5f52<\/strong>\uff1a\u7528\u4e8e\u56de\u5f52\u95ee\u9898\u3002<\/li>\n<li><strong>\u903b\u8f91\u56de\u5f52<\/strong>\uff1a\u7528\u4e8e\u4e8c\u5206\u7c7b\u95ee\u9898\u3002<\/li>\n<li><strong>\u51b3\u7b56\u6811<\/strong>\uff1a\u53ef\u4ee5\u7528\u4e8e\u5206\u7c7b\u548c\u56de\u5f52\u3002<\/li>\n<li><strong>\u968f\u673a\u68ee\u6797<\/strong>\uff1a\u4e00\u79cd\u96c6\u6210\u5b66\u4e60\u65b9\u6cd5\uff0c\u901a\u5e38\u6bd4\u5355\u4e00\u51b3\u7b56\u6811\u8868\u73b0\u66f4\u597d\u3002<\/li>\n<li><strong>\u652f\u6301\u5411\u91cf\u673a\uff08SVM\uff09<\/strong>\uff1a\u7528\u4e8e\u5206\u7c7b\u95ee\u9898\u3002<\/li>\n<li><strong>\u795e\u7ecf\u7f51\u7edc<\/strong>\uff1a\u7528\u4e8e\u590d\u6742\u7684\u5206\u7c7b\u548c\u56de\u5f52\u95ee\u9898\u3002<\/li>\n<\/ul>\n<p><p>\u9009\u62e9\u6a21\u578b\u65f6\u9700\u8981\u8003\u8651\u6570\u636e\u7684\u7279\u70b9\u3001\u95ee\u9898\u7684\u590d\u6742\u6027\u4ee5\u53ca\u8ba1\u7b97\u8d44\u6e90\u3002<\/p>\n<\/p>\n<p><p>\u56db\u3001\u8bad\u7ec3\u6a21\u578b<\/p>\n<\/p>\n<p><p>\u8bad\u7ec3\u6a21\u578b\u662f\u6307\u4f7f\u7528\u8bad\u7ec3\u6570\u636e\u6765\u62df\u5408\u6a21\u578b\u53c2\u6570\u3002\u901a\u5e38\uff0c\u6211\u4eec\u9700\u8981\u5c06\u6570\u636e\u96c6\u5206\u4e3a\u8bad\u7ec3\u96c6\u548c\u6d4b\u8bd5\u96c6\uff0c\u4ee5\u4fbf\u8bc4\u4f30\u6a21\u578b\u7684\u6027\u80fd\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">X = data.drop(&#39;target&#39;, axis=1)<\/p>\n<p>y = data[&#39;target&#39;]<\/p>\n<p>X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u63a5\u4e0b\u6765\uff0c\u9009\u62e9\u4e00\u4e2a\u6a21\u578b\u5e76\u8bad\u7ec3\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">model = LogisticRegression()<\/p>\n<p>model.fit(X_train, y_train)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u4e94\u3001\u8bc4\u4f30\u6a21\u578b\u6027\u80fd<\/p>\n<\/p>\n<p><p>\u8bc4\u4f30\u6a21\u578b\u7684\u6027\u80fd\u662f\u9a8c\u8bc1\u6a21\u578b\u662f\u5426\u6709\u6548\u7684\u91cd\u8981\u6b65\u9aa4\u3002\u5e38\u7528\u7684\u8bc4\u4f30\u6307\u6807\u6709\u51c6\u786e\u7387\u3001\u7cbe\u786e\u7387\u3001\u53ec\u56de\u7387\u3001F1\u5206\u6570\u7b49\u3002\u5bf9\u4e8e\u56de\u5f52\u95ee\u9898\uff0c\u53ef\u4ee5\u4f7f\u7528\u5747\u65b9\u8bef\u5dee\uff08MSE\uff09\u7b49\u6307\u6807\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">y_pred = model.predict(X_test)<\/p>\n<p>accuracy = accuracy_score(y_test, y_pred)<\/p>\n<p>print(f&#39;Accuracy: {accuracy}&#39;)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u516d\u3001\u4f18\u5316\u6a21\u578b<\/p>\n<\/p>\n<p><p>\u6a21\u578b\u4f18\u5316\u53ef\u4ee5\u901a\u8fc7\u8c03\u6574\u8d85\u53c2\u6570\u3001\u7279\u5f81\u9009\u62e9\u548c\u589e\u52a0\u6570\u636e\u91cf\u7b49\u65b9\u5f0f\u5b9e\u73b0\u3002\u8d85\u53c2\u6570\u8c03\u6574\u53ef\u4ee5\u4f7f\u7528\u7f51\u683c\u641c\u7d22\uff08Grid Search\uff09\u6216\u968f\u673a\u641c\u7d22\uff08Random Search\uff09\u7b49\u65b9\u6cd5\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">from sklearn.model_selection import GridSearchCV<\/p>\n<p>param_grid = {&#39;C&#39;: [0.1, 1, 10, 100]}<\/p>\n<p>grid = GridSearchCV(LogisticRegression(), param_grid, cv=5)<\/p>\n<p>grid.fit(X_train, y_train)<\/p>\n<p>print(grid.best_params_)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u4e03\u3001\u4fdd\u5b58\u6a21\u578b<\/p>\n<\/p>\n<p><p>\u5f53\u6a21\u578b\u8bad\u7ec3\u5b8c\u6210\u540e\uff0c\u53ef\u4ee5\u5c06\u6a21\u578b\u4fdd\u5b58\uff0c\u4ee5\u4fbf\u5728\u672a\u6765\u4f7f\u7528\u6216\u90e8\u7f72\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import joblib<\/p>\n<p>joblib.dump(model, &#39;model.pkl&#39;)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u603b\u7ed3\u6765\u8bf4\uff0c\u4f7f\u7528Python\u8bad\u7ec3\u6a21\u578b\u662f\u4e00\u4e2a\u7cfb\u7edf\u5316\u7684\u8fc7\u7a0b\uff0c\u9700\u8981\u6839\u636e\u5177\u4f53\u95ee\u9898\u9009\u62e9\u5408\u9002\u7684\u65b9\u6cd5\u548c\u5de5\u5177\u3002\u901a\u8fc7\u4e0d\u65ad\u7684\u5b9e\u8df5\u548c\u4f18\u5316\uff0c\u53ef\u4ee5\u63d0\u9ad8\u6a21\u578b\u7684\u51c6\u786e\u6027\u548c\u7a33\u5b9a\u6027\u3002\u91cd\u8981\u7684\u662f\uff0c\u6570\u636e\u8d28\u91cf\u548c\u6a21\u578b\u9009\u62e9\u5728\u6574\u4e2a\u8fc7\u7a0b\u4e2d\u8d77\u7740\u5173\u952e\u4f5c\u7528\u3002\u5e0c\u671b\u901a\u8fc7\u8fd9\u7bc7\u6587\u7ae0\uff0c\u4f60\u80fd\u5bf9\u5982\u4f55\u7528Python\u8bad\u7ec3\u6a21\u578b\u6709\u4e00\u4e2a\u66f4\u6e05\u6670\u7684\u7406\u89e3\u3002<\/p>\n<\/p>\n<h2><strong>\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n<p> <strong>\u5982\u4f55\u9009\u62e9\u5408\u9002\u7684\u673a\u5668\u5b66\u4e60\u6846\u67b6\u6765\u8bad\u7ec3\u6a21\u578b\uff1f<\/strong><br \/>\u5728Python\u4e2d\uff0c\u6709\u591a\u4e2a\u6d41\u884c\u7684\u673a\u5668\u5b66\u4e60\u6846\u67b6\u53ef\u4f9b\u9009\u62e9\uff0c\u5305\u62ecTensorFlow\u3001Keras\u548cScikit-Learn\u7b49\u3002\u9009\u62e9\u5408\u9002\u7684\u6846\u67b6\u5e94\u6839\u636e\u4f60\u7684\u9879\u76ee\u9700\u6c42\u3001\u6570\u636e\u7c7b\u578b\u548c\u6a21\u578b\u590d\u6742\u6027\u6765\u51b3\u5b9a\u3002TensorFlow\u548cKeras\u9002\u5408\u6df1\u5ea6\u5b66\u4e60\u4efb\u52a1\uff0c\u800cScikit-Learn\u5219\u66f4\u9002\u5408\u4f20\u7edf\u7684\u673a\u5668\u5b66\u4e60\u7b97\u6cd5\u3002\u4e86\u89e3\u5404\u6846\u67b6\u7684\u4f18\u7f3a\u70b9\u548c\u793e\u533a\u652f\u6301\u53ef\u4ee5\u5e2e\u52a9\u4f60\u505a\u51fa\u660e\u667a\u7684\u9009\u62e9\u3002<\/p>\n<p><strong>\u600e\u6837\u51c6\u5907\u6570\u636e\u4ee5\u4fbf\u4e8e\u6a21\u578b\u8bad\u7ec3\uff1f<\/strong><br 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