我正在使用scikit-learn中的DecisionTreeClassifier来对数据进行分类。我还使用了其他算法，并使用精确召回曲线下面积（AUPRC）来比较它们。问题是DecisionTreeClassifier的AUPRC形状是一个正方形，而不是这个指标通常应有的形状。
这是我如何计算DecisionTreeClassifier的AUPRC。由于DecisionTreeClassifier不像LogisticRegression等其他分类器那样具有decision_function()，我在这方面遇到了一些麻烦。
这是我对SVM、逻辑回归和DecisionTreeClassifier的AUPRC计算结果。
这是我如何计算DecisionTreeClassifier的AUPRC
def execute(X_train, y_train, X_test, y_test): tree = DecisionTreeClassifier(class_weight='balanced') tree_y_score = tree.fit(X_train, y_train).predict(X_test) tree_ap_score = average_precision_score(y_test, tree_y_score) precision, recall, _ = precision_recall_curve(y_test, tree_y_score) values = {'ap_score': tree_ap_score, 'precision': precision, 'recall': recall} return values这是我如何计算SVM的AUPRC：
def execute(X_train, y_train, X_test, y_test): svm = SVC(class_weight='balanced') svm.fit(X_train, y_train.values.ravel()) svm_y_score = svm.decision_function(X_test) svm_ap_score = average_precision_score(y_test, svm_y_score) precision, recall, _ = precision_recall_curve(y_test, svm_y_score) values = {'ap_score': svm_ap_score, 'precision': precision, 'recall': recall} return values这是我如何计算逻辑回归的AUPRC：
def execute(X_train, y_train, X_test, y_test): lr = LogisticRegression(class_weight='balanced') lr.fit(X_train, y_train.values.ravel()) lr_y_score = lr.decision_function(X_test) lr_ap_score = average_precision_score(y_test, lr_y_score) precision, recall, _ = precision_recall_curve(y_test, lr_y_score) values = {'ap_score': lr_ap_score, 'precision': precision, 'recall': recall} return values然后我调用这些方法并像这样绘制结果：
import LogReg_AP_Harness as lrApTestimport SVM_AP_Harness as svmApTestimport DecTree_AP_Harness as dtApTestfrom sklearn.model_selection import train_test_splitfrom sklearn.preprocessing import label_binarizeimport matplotlib.pyplot as pltdef do_work(df): X = df.ix[:, df.columns != 'Class'] y = df.ix[:, df.columns == 'Class'] y_binarized = label_binarize(y, classes=[0, 1]) n_classes = y_binarized.shape[1] X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.3, random_state=0) _, _, y_train_binarized, y_test_binarized = train_test_split(X, y_binarized, test_size=.3, random_state=0) print('Executing Logistic Regression') lr_values = lrApTest.execute(X_train, y_train, X_test, y_test) print('Executing Decision Tree') dt_values = dtApTest.execute(X_train, y_train_binarized, X_test, y_test_binarized) print('Executing SVM') svm_values = svmApTest.execute(X_train, y_train, X_test, y_test) plot_aupr_curves(lr_values, svm_values, dt_values)def plot_aupr_curves(lr_values, svm_values, dt_values): lr_ap_score = lr_values['ap_score'] lr_precision = lr_values['precision'] lr_recall = lr_values['recall'] svm_ap_score = svm_values['ap_score'] svm_precision = svm_values['precision'] svm_recall = svm_values['recall'] dt_ap_score = dt_values['ap_score'] dt_precision = dt_values['precision'] dt_recall = dt_values['recall'] plt.step(svm_recall, svm_precision, color='g', alpha=0.2,where='post') plt.fill_between(svm_recall, svm_precision, step='post', alpha=0.2, color='g') plt.step(lr_recall, lr_precision, color='b', alpha=0.2, where='post') plt.fill_between(lr_recall, lr_precision, step='post', alpha=0.2, color='b') plt.step(dt_recall, dt_precision, color='r', alpha=0.2, where='post') plt.fill_between(dt_recall, dt_precision, step='post', alpha=0.2, color='r') plt.xlabel('Recall') plt.ylabel('Precision') plt.ylim([0.0, 1.05]) plt.xlim([0.0, 1.0]) plt.title('SVM (Green): Precision-Recall curve: AP={0:0.2f}'.format(svm_ap_score) + '\n' + 'Logistic Regression (Blue): Precision-Recall curve: AP={0:0.2f}'.format(lr_ap_score) + '\n' + 'Decision Tree (Red): Precision-Recall curve: AP={0:0.2f}'.format(dt_ap_score)) plt.show()在do_work()方法中，我不得不对y进行二值化，因为DecisionTreeClassifier没有descision_function()。我的方法来自这里。
这是绘图结果：
我想归根结底是我错误地计算了DecisionTreeClassifier的AUPRC。
回答：
对于DecisionTreeClassifier，请将predict替换为predict_proba；后者起到与decision_function相同的作用。
