{"id":54025,"date":"2025-08-12T21:00:07","date_gmt":"2025-08-12T13:00:07","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/54025.html"},"modified":"2025-08-12T21:00:07","modified_gmt":"2025-08-12T13:00:07","slug":"%e9%9b%86%e6%88%90%e5%ad%a6%e4%b9%a0%e7%ae%97%e6%b3%95","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/54025.html","title":{"rendered":"\u96c6\u6210\u5b66\u4e60\u7b97\u6cd5"},"content":{"rendered":"

\"\"<\/p>\n

\"\"<\/p>\n<\/p>\n

\u4f5c\u4e1a\u4e00：\u57fa\u7840\u5e94\u7528 – \u9e22\u5c3e\u82b1\u5206\u7c7b<\/p>\n

\u200c\u4efb\u52a1\u76ee\u6807\u200c：<\/p>\n

\u4f7f\u7528\u968f\u673a\u68ee\u6797\u5bf9\u9e22\u5c3e\u82b1\u6570\u636e\u96c6\u8fdb\u884c\u5206\u7c7b，\u5e76\u5206\u6790\u7279\u5f81\u91cd\u8981\u6027<\/p>\n

\u200c\u6570\u636e\u96c6\u200c：<\/p>\n

sklearn.datasets.load_iris()<\/p>\n

\u200c\u8981\u6c42\u6b65\u9aa4\u200c：<\/p>\n

  • \u52a0\u8f7d\u9e22\u5c3e\u82b1\u6570\u636e\u96c6\u5e76\u5212\u5206\u8bad\u7ec3\u96c6\/\u6d4b\u8bd5\u96c6(70%\/30%)<\/li>\n
  • \u521b\u5efa\u968f\u673a\u68ee\u6797\u5206\u7c7b\u5668(\u8bbe\u7f6en_estimators=100, max_depth=3)<\/li>\n
  • \u8bad\u7ec3\u6a21\u578b\u5e76\u5728\u6d4b\u8bd5\u96c6\u4e0a\u8bc4\u4f30\u51c6\u786e\u7387<\/li>\n
  • \u8f93\u51fa\u5206\u7c7b\u62a5\u544a\u548c\u6df7\u6dc6\u77e9\u9635<\/li>\n
  • \u53ef\u89c6\u5316\u7279\u5f81\u91cd\u8981\u6027<\/li>\n
  • (\u9009\u505a)\u5c1d\u8bd5\u8c03\u6574n_estimators\u548cmax_depth\u89c2\u5bdf\u51c6\u786e\u7387\u53d8\u5316<\/li>\n<\/p>\n<\/p>\n<\/p>\n

    \u4f5c\u4e1a\u4e8c：\u4fe1\u7528\u5361\u6b3a\u8bc8\u68c0\u6d4b<\/p>\n

    \u200c\u4efb\u52a1\u76ee\u6807\u200c：<\/p>\n

    \u4f7f\u7528\u968f\u673a\u68ee\u6797\u5904\u7406\u7c7b\u522b\u4e0d\u5e73\u8861\u7684\u4fe1\u7528\u5361\u6b3a\u8bc8\u68c0\u6d4b\u95ee\u9898<\/p>\n

    \u200c\u6570\u636e\u96c6\u200c：<\/p>\n

    Kaggle\u4fe1\u7528\u5361\u6b3a\u8bc8\u6570\u636e\u96c6Credit Card Fraud<\/p>\n

    from sklearn.datasets import load_iris
    \nfrom sklearn.model_selection import train_test_split
    \nfrom sklearn.ensemble import RandomForestClassifier
    \nfrom sklearn.metrics import accuracy_score, classification_report, confusion_matrix
    \nimport matplotlib.pyplot as plt
    \nimport seaborn as sns
    \nimport numpy as np<\/p>\n

    # \u8bbe\u7f6e\u4e2d\u6587\u5b57\u4f53
    \nplt.rcParams["font.sans-serif"] = ["SimHei"]
    \nplt.rcParams["axes.unicode_minus"] = False<\/p>\n

    # 1. \u52a0\u8f7d\u9e22\u5c3e\u82b1\u6570\u636e\u96c6\u5e76\u5212\u5206\u8bad\u7ec3\u96c6\/\u6d4b\u8bd5\u96c6(70%\/30%)
    \niris = load_iris()
    \nX = iris.data
    \ny = iris.target<\/p>\n

    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)<\/p>\n

    # 2. \u521b\u5efa\u968f\u673a\u68ee\u6797\u5206\u7c7b\u5668(\u8bbe\u7f6en_estimators=100, max_depth=3)
    \nrf = RandomForestClassifier(n_estimators=100, max_depth=3, random_state=42)<\/p>\n

    # 3. \u8bad\u7ec3\u6a21\u578b\u5e76\u5728\u6d4b\u8bd5\u96c6\u4e0a\u8bc4\u4f30\u51c6\u786e\u7387
    \nrf.fit(X_train, y_train)
    \ny_pred = rf.predict(X_test)<\/p>\n

    accuracy = accuracy_score(y_test, y_pred)
    \nprint(f"\\\\n\u6a21\u578b\u51c6\u786e\u7387: {accuracy:.4f}")<\/p>\n

    # 4. \u8f93\u51fa\u5206\u7c7b\u62a5\u544a\u548c\u6df7\u6dc6\u77e9\u9635
    \nprint("\\\\n\u5206\u7c7b\u62a5\u544a:")
    \nprint(classification_report(y_test, y_pred, target_names=iris.target_names))<\/p>\n

    print("\\\\n\u6df7\u6dc6\u77e9\u9635:")
    \ncm = confusion_matrix(y_test, y_pred)
    \nprint(cm)<\/p>\n

    # \u53ef\u89c6\u5316\u6df7\u6dc6\u77e9\u9635
    \nplt.figure(figsize=(12, 5))<\/p>\n

    plt.subplot(1, 2, 1)
    \nsns.heatmap(cm, annot=True, fmt='d', cmap='Blues',
    \n xticklabels=iris.target_names,
    \n yticklabels=iris.target_names)
    \nplt.title('\u6df7\u6dc6\u77e9\u9635')
    \nplt.xlabel('\u9884\u6d4b\u6807\u7b7e')
    \nplt.ylabel('\u771f\u5b9e\u6807\u7b7e')<\/p>\n

    # 5. \u53ef\u89c6\u5316\u7279\u5f81\u91cd\u8981\u6027
    \nfeature_importance = rf.feature_importances_
    \nfeature_names = iris.feature_names<\/p>\n

    plt.subplot(1, 2, 2)
    \nindices = np.argsort(feature_importance)[::-1]
    \nplt.bar(range(len(feature_importance)), feature_importance[indices])
    \nplt.xticks(range(len(feature_importance)), [feature_names[i] for i in indices], rotation=45)
    \nplt.title('\u7279\u5f81\u91cd\u8981\u6027')
    \nplt.xlabel('\u7279\u5f81')
    \nplt.ylabel('\u91cd\u8981\u6027')<\/p>\n

    plt.tight_layout()
    \nplt.show()<\/p>\n

    # 6. (\u9009\u505a)\u5c1d\u8bd5\u8c03\u6574n_estimators\u548cmax_depth\u89c2\u5bdf\u51c6\u786e\u7387\u53d8\u5316
    \nprint("\\\\n\u9009\u505a\u90e8\u5206 – \u53c2\u6570\u8c03\u4f18:")
    \n# \u8c03\u6574n_estimators
    \nn_estimators_range = [10, 50, 100, 150, 200]
    \naccuracies_n_est = []<\/p>\n

    for n_est in n_estimators_range:
    \n rf_temp = RandomForestClassifier(n_estimators=n_est, max_depth=3, random_state=42)
    \n rf_temp.fit(X_train, y_train)
    \n y_pred_temp = rf_temp.predict(X_test)
    \n acc = accuracy_score(y_test, y_pred_temp)
    \n accuracies_n_est.append(acc)
    \n print(f"n_estimators={n_est}, \u51c6\u786e\u7387={acc:.4f}")<\/p>\n

    # \u8c03\u6574max_depth
    \nmax_depth_range = [1, 2, 3, 4, 5, 6, None]
    \naccuracies_max_depth = []<\/p>\n

    for max_d in max_depth_range:
    \n rf_temp = RandomForestClassifier(n_estimators=100, max_depth=max_d, random_state=42)
    \n rf_temp.fit(X_train, y_train)
    \n y_pred_temp = rf_temp.predict(X_test)
    \n acc = accuracy_score(y_test, y_pred_temp)
    \n accuracies_max_depth.append(acc)
    \n if max_d is None:
    \n print(f"max_depth=None, \u51c6\u786e\u7387={acc:.4f}")
    \n else:
    \n print(f"max_depth={max_d}, \u51c6\u786e\u7387={acc:.4f}")<\/p>\n

    # \u53ef\u89c6\u5316\u53c2\u6570\u8c03\u6574\u7ed3\u679c
    \nplt.figure(figsize=(12, 5))<\/p>\n

    plt.subplot(1, 2, 1)
    \nplt.plot(n_estimators_range, accuracies_n_est, marker='o')
    \nplt.title('n_estimators\u5bf9\u51c6\u786e\u7387\u7684\u5f71\u54cd')
    \nplt.xlabel('n_estimators')
    \nplt.ylabel('\u51c6\u786e\u7387')<\/p>\n

    plt.subplot(1, 2, 2)
    \nx_labels = [str(d) if d is not None else 'None' for d in max_depth_range]
    \nplt.plot(range(len(max_depth_range)), accuracies_max_depth, marker='o')
    \nplt.xticks(range(len(max_depth_range)), x_labels)
    \nplt.title('max_depth\u5bf9\u51c6\u786e\u7387\u7684\u5f71\u54cd')
    \nplt.xlabel('max_depth')
    \nplt.ylabel('\u51c6\u786e\u7387')<\/p>\n

    plt.tight_layout()
    \nplt.show() <\/p>\n

    Credit Card Fraud<\/p>\n

    \u200c\u8981\u6c42\u6b65\u9aa4\u200c：<\/p>\n

  • \u52a0\u8f7d\u4fe1\u7528\u5361\u4ea4\u6613\u6570\u636e(\u6ce8\u610f\u6570\u636e\u9ad8\u5ea6\u4e0d\u5e73\u8861)<\/li>\n
  • \u6807\u51c6\u5316Amount\u7279\u5f81，Time\u7279\u5f81\u53ef\u5220\u9664<\/li>\n
  • \u4f7f\u7528\u5206\u5c42\u62bd\u6837\u5212\u5206\u8bad\u7ec3\u96c6\/\u6d4b\u8bd5\u96c6<\/li>\n
  • \u521b\u5efa\u968f\u673a\u68ee\u6797\u5206\u7c7b\u5668(class_weight='balanced')<\/li>\n
  • \u8bc4\u4f30\u6a21\u578b(\u4f7f\u7528\u7cbe\u786e\u7387\u3001\u53ec\u56de\u7387\u3001F1\u3001AUC-ROC)<\/li>\n

    import pandas as pd
    \nfrom sklearn.model_selection import train_test_split
    \nfrom sklearn.preprocessing import StandardScaler
    \nfrom sklearn.ensemble import RandomForestClassifier
    \nfrom sklearn.metrics import classification_report, roc_auc_score, precision_score, recall_score, f1_score<\/p>\n

    # \u6b65\u9aa41: \u52a0\u8f7d\u4fe1\u7528\u5361\u4ea4\u6613\u6570\u636e
    \n# \u6ce8\u610f：\u9700\u8981\u5148\u4e0b\u8f7d\u6570\u636e\u96c6\u5e76\u653e\u5728\u5f53\u524d\u76ee\u5f55\u4e0b
    \ndf = pd.read_csv("creditcard.csv")<\/p>\n

    # \u68c0\u67e5\u6570\u636e\u4e0d\u5e73\u8861\u60c5\u51b5
    \nprint("\u6570\u636e\u96c6\u5927\u5c0f:", df.shape)
    \nprint("\u7c7b\u522b\u5206\u5e03:")
    \nprint(df['Class'].value_counts())
    \nprint("\u6b3a\u8bc8\u4ea4\u6613\u6bd4\u4f8b: {:.4f}%".format(df['Class'].sum() \/ len(df) * 100))<\/p>\n

    # \u6b65\u9aa42: \u6807\u51c6\u5316Amount\u7279\u5f81，\u5220\u9664Time\u7279\u5f81
    \n# \u5220\u9664Time\u5217
    \ndf = df.drop(['Time'], axis=1)<\/p>\n

    # \u6807\u51c6\u5316Amount\u5217
    \nscaler = StandardScaler()
    \ndf['Amount'] = scaler.fit_transform(df['Amount'].values.reshape(-1, 1))<\/p>\n

    # \u5206\u79bb\u7279\u5f81\u548c\u6807\u7b7e
    \nX = df.drop('Class', axis=1)
    \ny = df['Class']<\/p>\n

    # \u6b65\u9aa43: \u4f7f\u7528\u5206\u5c42\u62bd\u6837\u5212\u5206\u8bad\u7ec3\u96c6\/\u6d4b\u8bd5\u96c6
    \nX_train, X_test, y_train, y_test = train_test_split(
    \n X, y, test_size=0.2, random_state=42, stratify=y)<\/p>\n

    print("\u8bad\u7ec3\u96c6\u5927\u5c0f:", X_train.shape)
    \nprint("\u6d4b\u8bd5\u96c6\u5927\u5c0f:", X_test.shape)
    \nprint("\u8bad\u7ec3\u96c6\u6b3a\u8bc8\u4ea4\u6613\u6570:", sum(y_train))
    \nprint("\u6d4b\u8bd5\u96c6\u6b3a\u8bc8\u4ea4\u6613\u6570:", sum(y_test))<\/p>\n

    # \u6b65\u9aa44: \u521b\u5efa\u968f\u673a\u68ee\u6797\u5206\u7c7b\u5668
    \nrf_classifier = RandomForestClassifier(
    \n n_estimators=100,
    \n class_weight='balanced', # \u5904\u7406\u7c7b\u522b\u4e0d\u5e73\u8861
    \n random_state=42
    \n)<\/p>\n

    # \u8bad\u7ec3\u6a21\u578b
    \nrf_classifier.fit(X_train, y_train)<\/p>\n

    # \u6b65\u9aa45: \u8bc4\u4f30\u6a21\u578b
    \n# \u9884\u6d4b
    \ny_pred = rf_classifier.predict(X_test)
    \ny_pred_proba = rf_classifier.predict_proba(X_test)[:, 1]<\/p>\n

    # \u8ba1\u7b97\u8bc4\u4f30\u6307\u6807
    \nprecision = precision_score(y_test, y_pred)
    \nrecall = recall_score(y_test, y_pred)
    \nf1 = f1_score(y_test, y_pred)
    \nauc_roc = roc_auc_score(y_test, y_pred_proba)<\/p>\n

    print("\\\\n\u6a21\u578b\u8bc4\u4f30\u7ed3\u679c:")
    \nprint("\u7cbe\u786e\u7387(Precision): {:.4f}".format(precision))
    \nprint("\u53ec\u56de\u7387(Recall): {:.4f}".format(recall))
    \nprint("F1\u5206\u6570: {:.4f}".format(f1))
    \nprint("AUC-ROC: {:.4f}".format(auc_roc))<\/p>\n

    # \u8be6\u7ec6\u5206\u7c7b\u62a5\u544a
    \nprint("\\\\n\u8be6\u7ec6\u5206\u7c7b\u62a5\u544a:")
    \nprint(classification_report(y_test, y_pred)) <\/p>\n<\/p>\n<\/p>\n<\/p>\n<\/p>\n<\/p>\n<\/p>\n<\/p>\n<\/p>\n<\/p>\n<\/p>\n<\/p>\n<\/p>\n<\/p>\n<\/p>\n<\/p>\n<\/p>\n<\/p>\n<\/p><\/p>\n","protected":false},"excerpt":{"rendered":"

    \u6587\u7ae0\u6d4f\u89c8\u9605\u8bfb185\u6b21\u3002\u3010\u4ee3\u7801\u3011\u96c6\u6210\u5b66\u4e60\u7b97\u6cd5\u3002<\/p>\n","protected":false},"author":2,"featured_media":54023,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[50,427,209],"topic":[],"class_list":["post-54025","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-server","tag-50","tag-427","tag-209"],"yoast_head":"\n\u96c6\u6210\u5b66\u4e60\u7b97\u6cd5 - \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\/54025.html\" \/>\n<meta property=\"og:locale\" content=\"zh_CN\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"\u96c6\u6210\u5b66\u4e60\u7b97\u6cd5 - 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