{"id":949065,"date":"2024-12-27T00:09:24","date_gmt":"2024-12-26T16:09:24","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/949065.html"},"modified":"2024-12-27T00:09:26","modified_gmt":"2024-12-26T16:09:26","slug":"svm%e5%a6%82%e4%bd%95%e8%b0%83%e7%94%a8python","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/949065.html","title":{"rendered":"svm\u5982\u4f55\u8c03\u7528python"},"content":{"rendered":"

\"svm\u5982\u4f55\u8c03\u7528python\"<\/p>\n

\u4f7f\u7528Python\u8c03\u7528SVM\u7684\u6b65\u9aa4\u5305\u62ec\uff1a\u5b89\u88c5\u5fc5\u8981\u7684\u5e93\u3001\u5bfc\u5165\u5e93\u3001\u51c6\u5907\u6570\u636e\u3001\u521b\u5efaSVM\u6a21\u578b\u3001\u8bad\u7ec3\u6a21\u578b\u3001\u8bc4\u4f30\u6a21\u578b\u6027\u80fd\u3002\u4ee5\u4e0b\u5c06\u8be6\u7ec6\u8bb2\u8ff0\u5982\u4f55\u4f7f\u7528Python\u8c03\u7528SVM\u8fdb\u884c\u5206\u7c7b\u4efb\u52a1\u3002<\/strong><\/p>\n<\/p>\n

Python\u662f\u4e00\u79cd\u5f3a\u5927\u7684\u7f16\u7a0b\u8bed\u8a00\uff0c\u5e7f\u6cdb\u5e94\u7528\u4e8e\u6570\u636e\u79d1\u5b66\u548c\u673a\u5668\u5b66\u4e60<\/a>\u9886\u57df\u3002SVM\uff08\u652f\u6301\u5411\u91cf\u673a\uff09\u662f\u4e00\u79cd\u5e38\u7528\u7684\u673a\u5668\u5b66\u4e60\u7b97\u6cd5\uff0c\u7279\u522b\u9002\u7528\u4e8e\u5206\u7c7b\u95ee\u9898\u3002\u4f7f\u7528Python\u8c03\u7528SVM\u4e3b\u8981\u6d89\u53ca\u5230\u51e0\u4e2a\u5173\u952e\u6b65\u9aa4\uff1a\u5b89\u88c5\u6240\u9700\u7684\u5e93\u3001\u6570\u636e\u51c6\u5907\u3001\u6a21\u578b\u8bad\u7ec3\u548c\u8bc4\u4f30\u3002\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u5c06\u8be6\u7ec6\u63a2\u8ba8\u8fd9\u4e9b\u6b65\u9aa4\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u5b89\u88c5\u548c\u5bfc\u5165\u5e93<\/p>\n<\/p>\n

\u8981\u4f7f\u7528Python\u4e2d\u7684SVM\uff0c\u6211\u4eec\u9996\u5148\u9700\u8981\u5b89\u88c5\u5e76\u5bfc\u5165\u76f8\u5173\u7684\u5e93\u3002\u6700\u5e38\u7528\u7684\u5e93\u662fscikit-learn<\/code>\uff0c\u5b83\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u673a\u5668\u5b66\u4e60\u7b97\u6cd5\u548c\u5de5\u5177\u3002<\/p>\n<\/p>\n

    \n
  1. \u5b89\u88c5scikit-learn<\/li>\n<\/ol>\n

    \u8981\u4f7f\u7528scikit-learn\u5e93\uff0c\u9700\u8981\u786e\u4fdd\u5b83\u5df2\u7ecf\u5b89\u88c5\u3002\u5982\u679c\u8fd8\u6ca1\u6709\u5b89\u88c5\uff0c\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u547d\u4ee4\u6765\u5b89\u88c5\uff1a<\/p>\n<\/p>\n

    pip install scikit-learn<\/p>\n
    <\/code><\/pre>\n<\/p>\n
      \n
    1. \u5bfc\u5165\u5fc5\u8981\u7684\u5e93<\/li>\n<\/ol>\n
      \u5728Python\u811a\u672c\u4e2d\uff0c\u6211\u4eec\u9700\u8981\u5bfc\u5165\u76f8\u5173\u5e93\u3002\u901a\u5e38\uff0c\u6211\u4eec\u9700\u8981\u5bfc\u5165numpy\u3001pandas\uff08\u7528\u4e8e\u6570\u636e\u5904\u7406\uff09\u548csklearn\u5e93\u4e2d\u7684\u76f8\u5173\u6a21\u5757\u3002<\/p>\n<\/p>\n
      import numpy as np<\/p>\n
      import pandas as pd<\/p>\n
      from sklearn import datasets<\/p>\n
      from sklearn.model_selection import trAI<\/a>n_test_split<\/p>\n
      from sklearn.svm import SVC<\/p>\n
      from sklearn.metrics import accuracy_score, classification_report<\/p>\n
      <\/code><\/pre>\n<\/p>\n
      \u4e8c\u3001\u51c6\u5907\u6570\u636e<\/p>\n<\/p>\n
      \u5728\u5e94\u7528SVM\u4e4b\u524d\uff0c\u6211\u4eec\u9700\u8981\u51c6\u5907\u597d\u6570\u636e\u96c6\u3002\u6570\u636e\u51c6\u5907\u5305\u62ec\u6570\u636e\u52a0\u8f7d\u3001\u6570\u636e\u6e05\u6d17\u548c\u6570\u636e\u5206\u5272\u3002<\/p>\n<\/p>\n
        \n
      1. \u6570\u636e\u52a0\u8f7d<\/li>\n<\/ol>\n
        \u53ef\u4ee5\u4f7f\u7528scikit-learn\u81ea\u5e26\u7684\u6570\u636e\u96c6\uff0c\u4f8b\u5982\u9e22\u5c3e\u82b1\u6570\u636e\u96c6\u3002\u4e5f\u53ef\u4ee5\u4ece\u672c\u5730\u6216\u7f51\u7edc\u52a0\u8f7d\u81ea\u5df1\u7684\u6570\u636e\u96c6\u3002<\/p>\n<\/p>\n
        # \u52a0\u8f7d\u9e22\u5c3e\u82b1\u6570\u636e\u96c6<\/p>\n
        iris = datasets.load_iris()<\/p>\n
        X = iris.data<\/p>\n
        y = iris.target<\/p>\n
        <\/code><\/pre>\n<\/p>\n
          \n
        1. \u6570\u636e\u5206\u5272<\/li>\n<\/ol>\n
          \u4e3a\u4e86\u8bc4\u4f30\u6a21\u578b\u6027\u80fd\uff0c\u9700\u8981\u5c06\u6570\u636e\u96c6\u5206\u4e3a\u8bad\u7ec3\u96c6\u548c\u6d4b\u8bd5\u96c6\u3002<\/p>\n<\/p>\n
          # \u5c06\u6570\u636e\u96c6\u5206\u4e3a\u8bad\u7ec3\u96c6\u548c\u6d4b\u8bd5\u96c6<\/p>\n
          X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)<\/p>\n
          <\/code><\/pre>\n<\/p>\n
          \u4e09\u3001\u521b\u5efa\u548c\u8bad\u7ec3SVM\u6a21\u578b<\/p>\n<\/p>\n
          \u5728\u51c6\u5907\u597d\u6570\u636e\u540e\uff0c\u6211\u4eec\u53ef\u4ee5\u521b\u5efaSVM\u6a21\u578b\u5e76\u8fdb\u884c\u8bad\u7ec3\u3002<\/p>\n<\/p>\n
            \n
          1. \u521b\u5efaSVM\u6a21\u578b<\/li>\n<\/ol>\n
            \u4f7f\u7528SVC<\/code>\u7c7b\u6765\u521b\u5efaSVM\u6a21\u578b\u3002\u53ef\u4ee5\u6307\u5b9a\u6838\u51fd\u6570\uff08kernel\uff09\uff0c\u4f8b\u5982\u7ebf\u6027\u6838\u3001RBF\u6838\u7b49\u3002<\/p>\n<\/p>\n
            # \u521b\u5efaSVM\u6a21\u578b<\/p>\n
            svm_model = SVC(kernel='linear', C=1.0, random_state=42)<\/p>\n
            <\/code><\/pre>\n<\/p>\n
              \n
            1. \u8bad\u7ec3\u6a21\u578b<\/li>\n<\/ol>\n
              \u4f7f\u7528\u8bad\u7ec3\u6570\u636e\u5bf9\u6a21\u578b\u8fdb\u884c\u8bad\u7ec3\u3002<\/p>\n<\/p>\n
              # \u8bad\u7ec3\u6a21\u578b<\/p>\n
              svm_model.fit(X_train, y_train)<\/p>\n
              <\/code><\/pre>\n<\/p>\n
              \u56db\u3001\u8bc4\u4f30\u6a21\u578b\u6027\u80fd<\/p>\n<\/p>\n
              \u5728\u6a21\u578b\u8bad\u7ec3\u5b8c\u6210\u540e\uff0c\u9700\u8981\u4f7f\u7528\u6d4b\u8bd5\u96c6\u8bc4\u4f30\u5176\u6027\u80fd\u3002<\/p>\n<\/p>\n
                \n
              1. \u9884\u6d4b\u6d4b\u8bd5\u96c6<\/li>\n<\/ol>\n
                \u4f7f\u7528\u8bad\u7ec3\u597d\u7684\u6a21\u578b\u5bf9\u6d4b\u8bd5\u96c6\u8fdb\u884c\u9884\u6d4b\u3002<\/p>\n<\/p>\n
                # \u9884\u6d4b\u6d4b\u8bd5\u96c6<\/p>\n
                y_pred = svm_model.predict(X_test)<\/p>\n
                <\/code><\/pre>\n<\/p>\n
                  \n
                1. \u8bc4\u4f30\u6a21\u578b\u6027\u80fd<\/li>\n<\/ol>\n
                  \u901a\u8fc7\u51c6\u786e\u7387\u548c\u5206\u7c7b\u62a5\u544a\u6765\u8bc4\u4f30\u6a21\u578b\u6027\u80fd\u3002<\/p>\n<\/p>\n
                  # \u8ba1\u7b97\u51c6\u786e\u7387<\/p>\n
                  accuracy = accuracy_score(y_test, y_pred)<\/p>\n
                  print(f"Accuracy: {accuracy}")<\/p>\n
                  \u6253\u5370\u5206\u7c7b\u62a5\u544a<\/strong><\/h2>\n
                  print(classification_report(y_test, y_pred))<\/p>\n
                  <\/code><\/pre>\n<\/p>\n
                  \u4e94\u3001SVM\u53c2\u6570\u8c03\u4f18<\/p>\n<\/p>\n
                  \u4e3a\u4e86\u63d0\u5347SVM\u6a21\u578b\u7684\u6027\u80fd\uff0c\u53ef\u4ee5\u5bf9\u5176\u53c2\u6570\u8fdb\u884c\u8c03\u4f18\u3002\u5e38\u89c1\u7684\u53c2\u6570\u5305\u62ec\u6838\u51fd\u6570\u7c7b\u578b\u3001C\u53c2\u6570\u548cgamma\u53c2\u6570\u3002<\/p>\n<\/p>\n
                    \n
                  1. \u6838\u51fd\u6570\u7c7b\u578b<\/li>\n<\/ol>\n
                    SVM\u652f\u6301\u591a\u79cd\u6838\u51fd\u6570\uff0c\u5305\u62ec\u7ebf\u6027\u6838\u3001\u591a\u9879\u5f0f\u6838\u3001RBF\u6838\u7b49\u3002\u9009\u62e9\u5408\u9002\u7684\u6838\u51fd\u6570\u53ef\u4ee5\u63d0\u5347\u6a21\u578b\u7684\u8868\u73b0\u3002<\/p>\n<\/p>\n
                    # \u4f7f\u7528RBF\u6838<\/p>\n
                    svm_model_rbf = SVC(kernel='rbf', C=1.0, gamma='scale', random_state=42)<\/p>\n
                    svm_model_rbf.fit(X_train, y_train)<\/p>\n
                    y_pred_rbf = svm_model_rbf.predict(X_test)<\/p>\n
                    print(f"RBF Kernel Accuracy: {accuracy_score(y_test, y_pred_rbf)}")<\/p>\n
                    <\/code><\/pre>\n<\/p>\n
                      \n
                    1. C\u53c2\u6570<\/li>\n<\/ol>\n
                      C\u53c2\u6570\u662f\u5bf9\u9519\u8bef\u5206\u7c7b\u7684\u60e9\u7f5a\u7a0b\u5ea6\u3002\u8f83\u5927\u7684C\u503c\u4f1a\u4f7f\u6a21\u578b\u66f4\u52a0\u6ce8\u91cd\u6b63\u786e\u5206\u7c7b\u8bad\u7ec3\u6570\u636e\uff0c\u4f46\u53ef\u80fd\u5bfc\u81f4\u8fc7\u62df\u5408\u3002<\/p>\n<\/p>\n
                      # \u8c03\u6574C\u53c2\u6570<\/p>\n
                      svm_model_high_C = SVC(kernel='linear', C=10.0, random_state=42)<\/p>\n
                      svm_model_high_C.fit(X_train, y_train)<\/p>\n
                      y_pred_high_C = svm_model_high_C.predict(X_test)<\/p>\n
                      print(f"High C Accuracy: {accuracy_score(y_test, y_pred_high_C)}")<\/p>\n
                      <\/code><\/pre>\n<\/p>\n
                        \n
                      1. gamma\u53c2\u6570<\/li>\n<\/ol>\n
                        \u5bf9\u4e8eRBF\u6838\uff0cgamma\u53c2\u6570\u5b9a\u4e49\u4e86\u5355\u4e2a\u8bad\u7ec3\u6837\u672c\u7684\u5f71\u54cd\u8303\u56f4\u3002\u8f83\u5c0f\u7684gamma\u503c\u8868\u793a\u8f83\u5927\u7684\u5f71\u54cd\u8303\u56f4\u3002<\/p>\n<\/p>\n
                        # \u8c03\u6574gamma\u53c2\u6570<\/p>\n
                        svm_model_low_gamma = SVC(kernel='rbf', C=1.0, gamma=0.1, random_state=42)<\/p>\n
                        svm_model_low_gamma.fit(X_train, y_train)<\/p>\n
                        y_pred_low_gamma = svm_model_low_gamma.predict(X_test)<\/p>\n
                        print(f"Low Gamma Accuracy: {accuracy_score(y_test, y_pred_low_gamma)}")<\/p>\n
                        <\/code><\/pre>\n<\/p>\n
                        \u516d\u3001\u4ea4\u53c9\u9a8c\u8bc1\u4e0e\u6a21\u578b\u9009\u62e9<\/p>\n<\/p>\n
                        \u4e3a\u4e86\u66f4\u597d\u5730\u8bc4\u4f30\u6a21\u578b\u6027\u80fd\uff0c\u53ef\u4ee5\u4f7f\u7528\u4ea4\u53c9\u9a8c\u8bc1\u6765\u9009\u62e9\u6700\u4f73\u6a21\u578b\u3002<\/p>\n<\/p>\n
                          \n
                        1. \u4ea4\u53c9\u9a8c\u8bc1<\/li>\n<\/ol>\n
                          \u4ea4\u53c9\u9a8c\u8bc1\u53ef\u4ee5\u5e2e\u52a9\u6211\u4eec\u66f4\u5168\u9762\u5730\u8bc4\u4f30\u6a21\u578b\u6027\u80fd\uff0c\u51cf\u5c11\u6570\u636e\u5206\u5272\u5e26\u6765\u7684\u504f\u5dee\u3002<\/p>\n<\/p>\n
                          from sklearn.model_selection import cross_val_score<\/p>\n
                          \u4ea4\u53c9\u9a8c\u8bc1<\/strong><\/h2>\n
                          scores = cross_val_score(svm_model, X, y, cv=5)<\/p>\n
                          print(f"Cross-Validated Accuracy: {np.mean(scores)}")<\/p>\n
                          <\/code><\/pre>\n<\/p>\n
                            \n
                          1. \u7f51\u683c\u641c\u7d22<\/li>\n<\/ol>\n
                            \u7f51\u683c\u641c\u7d22\u53ef\u4ee5\u5e2e\u52a9\u6211\u4eec\u81ea\u52a8\u5316\u53c2\u6570\u8c03\u4f18\u8fc7\u7a0b\uff0c\u627e\u5230\u6700\u4f73\u7684\u53c2\u6570\u7ec4\u5408\u3002<\/p>\n<\/p>\n
                            from sklearn.model_selection import GridSearchCV<\/p>\n
                            \u5b9a\u4e49\u53c2\u6570\u7f51\u683c<\/strong><\/h2>\n
                            param_grid = {<\/p>\n
                                'kernel': ['linear', 'rbf'],<\/p>\n
                                'C': [0.1, 1.0, 10.0],<\/p>\n
                                'gamma': ['scale', 0.1, 1.0]<\/p>\n
                            }<\/p>\n
                            \u8fdb\u884c\u7f51\u683c\u641c\u7d22<\/strong><\/h2>\n
                            grid_search = GridSearchCV(SVC(random_state=42), param_grid, cv=5)<\/p>\n
                            grid_search.fit(X_train, y_train)<\/p>\n
                            \u8f93\u51fa\u6700\u4f73\u53c2\u6570<\/strong><\/h2>\n
                            print(f"Best Parameters: {grid_search.best_params_}")<\/p>\n
                            <\/code><\/pre>\n<\/p>\n
                            \u4e03\u3001\u603b\u7ed3\u4e0e\u5e94\u7528<\/p>\n<\/p>\n
                            \u652f\u6301\u5411\u91cf\u673a\u662f\u4e00\u79cd\u5f3a\u5927\u7684\u5206\u7c7b\u5de5\u5177\uff0c\u7279\u522b\u9002\u7528\u4e8e\u9ad8\u7ef4\u6570\u636e\u3002\u901a\u8fc7\u5408\u7406\u7684\u53c2\u6570\u8c03\u4f18\u548c\u4ea4\u53c9\u9a8c\u8bc1\uff0c\u53ef\u4ee5\u663e\u8457\u63d0\u5347\u6a21\u578b\u7684\u6027\u80fd\u3002\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u9009\u62e9\u5408\u9002\u7684\u6838\u51fd\u6570\u548c\u53c2\u6570\u8bbe\u7f6e\u81f3\u5173\u91cd\u8981\u3002\u6b64\u5916\uff0cSVM\u4e5f\u53ef\u4ee5\u7528\u4e8e\u56de\u5f52\u95ee\u9898\uff08SVR\uff09\u548c\u5f02\u5e38\u68c0\u6d4b\uff08One-Class SVM\uff09\uff0c\u6269\u5c55\u4e86\u5176\u5e94\u7528\u8303\u56f4\u3002<\/p>\n<\/p>\n
                            \u5728\u4f7f\u7528Python\u8c03\u7528SVM\u65f6\uff0c\u5e94\u6839\u636e\u5177\u4f53\u95ee\u9898\u7279\u70b9\u548c\u6570\u636e\u96c6\u7279\u6027\u8fdb\u884c\u53c2\u6570\u8c03\u6574\uff0c\u4ee5\u83b7\u5f97\u6700\u4f73\u7684\u6a21\u578b\u8868\u73b0\u3002\u901a\u8fc7\u79d1\u5b66\u7684\u65b9\u6cd5\u548c\u5de5\u5177\uff0cSVM\u53ef\u4ee5\u6210\u4e3a\u89e3\u51b3\u590d\u6742\u5206\u7c7b\u95ee\u9898\u7684\u6709\u529b\u5de5\u5177\u3002<\/p>\n<\/p>\n
                            \u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
                             \u5982\u4f55\u5728Python\u4e2d\u4f7f\u7528SVM\u8fdb\u884c\u5206\u7c7b\u4efb\u52a1\uff1f<\/strong>
                            \u5728Python\u4e2d\u4f7f\u7528\u652f\u6301\u5411\u91cf\u673a\uff08SVM\uff09\u8fdb\u884c\u5206\u7c7b\u4efb\u52a1\uff0c\u901a\u5e38\u53ef\u4ee5\u4f7f\u7528scikit-learn<\/code>\u5e93\u3002\u9996\u5148\uff0c\u9700\u8981\u5b89\u88c5\u8be5\u5e93\uff0c\u53ef\u4ee5\u901a\u8fc7pip install scikit-learn<\/code>\u8fdb\u884c\u5b89\u88c5\u3002\u63a5\u4e0b\u6765\uff0c\u5bfc\u5165\u6240\u9700\u7684\u6a21\u5757\uff0c\u52a0\u8f7d\u6570\u636e\u96c6\uff0c\u9009\u62e9SVM\u5206\u7c7b\u5668\uff08\u5982SVC<\/code>\uff09\uff0c\u5e76\u4f7f\u7528fit<\/code>\u65b9\u6cd5\u8bad\u7ec3\u6a21\u578b\u3002\u5b8c\u6210\u8bad\u7ec3\u540e\uff0c\u4f7f\u7528predict<\/code>\u65b9\u6cd5\u5bf9\u65b0\u6570\u636e\u8fdb\u884c\u9884\u6d4b\u3002\u793a\u4f8b\u4ee3\u7801\u5982\u4e0b\uff1a<\/p>\n
                            from sklearn import datasets\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.svm import SVC\n\n# \u52a0\u8f7d\u6570\u636e\u96c6\niris = datasets.load_iris()\nX = iris.data\ny = iris.target\n\n# \u5212\u5206\u6570\u636e\u96c6\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n\n# \u521b\u5efaSVM\u5206\u7c7b\u5668\nmodel = SVC(kernel='linear')\n\n# \u8bad\u7ec3\u6a21\u578b\nmodel.fit(X_train, y_train)\n\n# \u8fdb\u884c\u9884\u6d4b\npredictions = model.predict(X_test)\n<\/code><\/pre>\n
                            \u5728Python\u4e2d\u5982\u4f55\u8c03\u6574SVM\u7684\u8d85\u53c2\u6570\u4ee5\u63d0\u9ad8\u6027\u80fd\uff1f<\/strong>
                            \u8c03\u6574SVM\u7684\u8d85\u53c2\u6570\u662f\u63d0\u5347\u6a21\u578b\u6027\u80fd\u7684\u91cd\u8981\u4e00\u6b65\u3002\u5e38\u89c1\u7684\u8d85\u53c2\u6570\u5305\u62ecC<\/code>\uff08\u60e9\u7f5a\u53c2\u6570\uff09\u3001kernel<\/code>\uff08\u6838\u51fd\u6570\u7c7b\u578b\uff09\u3001gamma<\/code>\uff08\u6838\u51fd\u6570\u7684\u7cfb\u6570\uff09\u7b49\u3002\u53ef\u4ee5\u4f7f\u7528GridSearchCV<\/code>\u8fdb\u884c\u8d85\u53c2\u6570\u4f18\u5316\uff0c\u5b83\u4f1a\u901a\u8fc7\u4ea4\u53c9\u9a8c\u8bc1\u627e\u5230\u6700\u4f73\u53c2\u6570\u7ec4\u5408\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u793a\u4f8b\uff1a<\/p>\n
                            from sklearn.model_selection import GridSearchCV\n\nparam_grid = {\n    'C': [0.1, 1, 10],\n    'kernel': ['linear', 'rbf'],\n    'gamma': ['scale', 'auto']\n}\n\ngrid_search = GridSearchCV(SVC(), param_grid, cv=5)\ngrid_search.fit(X_train, y_train)\n\nprint("\u6700\u4f73\u53c2\u6570:", grid_search.best_params_)\n<\/code><\/pre>\n
                            \u5982\u4f55\u8bc4\u4f30\u4f7f\u7528SVM\u8bad\u7ec3\u7684\u6a21\u578b\u7684\u6548\u679c\uff1f<\/strong>
                            \u8bc4\u4f30SVM\u6a21\u578b\u6548\u679c\u7684\u5e38\u7528\u65b9\u6cd5\u5305\u62ec\u51c6\u786e\u7387\u3001\u7cbe\u786e\u7387\u3001\u53ec\u56de\u7387\u3001F1\u5206\u6570\u7b49\u3002\u53ef\u4ee5\u4f7f\u7528sklearn.metrics<\/code>\u6a21\u5757\u4e2d\u7684\u76f8\u5173\u51fd\u6570\u6765\u8ba1\u7b97\u8fd9\u4e9b\u6307\u6807\u3002\u901a\u8fc7\u5c06\u9884\u6d4b\u7ed3\u679c\u4e0e\u5b9e\u9645\u6807\u7b7e\u8fdb\u884c\u6bd4\u8f83\uff0c\u53ef\u4ee5\u751f\u6210\u6df7\u6dc6\u77e9\u9635\uff0c\u5e2e\u52a9\u66f4\u76f4\u89c2\u5730\u7406\u89e3\u6a21\u578b\u7684\u6027\u80fd\u3002\u793a\u4f8b\u4ee3\u7801\u5982\u4e0b\uff1a<\/p>\n
                            from sklearn.metrics import classification_report, confusion_matrix\n\n# \u8f93\u51fa\u6df7\u6dc6\u77e9\u9635\nconfusion = confusion_matrix(y_test, predictions)\nprint("\u6df7\u6dc6\u77e9\u9635:\\n", confusion)\n\n# \u8f93\u51fa\u5206\u7c7b\u62a5\u544a\nreport = classification_report(y_test, predictions)\nprint("\u5206\u7c7b\u62a5\u544a:\\n", report)\n<\/code><\/pre>\n","protected":false},"excerpt":{"rendered":"\u4f7f\u7528Python\u8c03\u7528SVM\u7684\u6b65\u9aa4\u5305\u62ec\uff1a\u5b89\u88c5\u5fc5\u8981\u7684\u5e93\u3001\u5bfc\u5165\u5e93\u3001\u51c6\u5907\u6570\u636e\u3001\u521b\u5efaSVM\u6a21\u578b\u3001\u8bad\u7ec3\u6a21\u578b\u3001\u8bc4\u4f30\u6a21\u578b\u6027\u80fd\u3002 […]","protected":false},"author":3,"featured_media":949076,"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\/949065"}],"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=949065"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/949065\/revisions"}],"predecessor-version":[{"id":949082,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/949065\/revisions\/949082"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/949076"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=949065"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=949065"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=949065"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}