from sklearn.datasets import load_iris from mlxtend.feature_selection import ColumnSelector from sklearn.pipeline import make_pipeline iris = load_iris() X = iris.data[:100] y = iris.target[:100] pipe1 = make_pipeline(ColumnSelector(cols=(0, 2)), LogisticRegression()) pipe2 = make_pipeline(ColumnSelector(cols=(1, 2, 3)), LogisticRegression()) sclf = StackingClassifier(classifiers=[pipe1, pipe2], meta_classifier=LogisticRegression()) sclf.fit(X, y) decision_scores = sclf.decision_function(X) print("Val auc Score of Stacking: %f" % (roc_auc_score(y, sclf.predict_proba(X)[:, 1]))) fig, axe = plt.subplots(2, 2, figsize=(30, 20)) rlb.ComprehensiveIndicatorFigure(y, decision_scores, axe[0], 1) rlb.ComprehensiveIndicatorSkLibFigure(y, decision_scores, axe[1]) # In[]: # 5、ROC Curve with decision_function from sklearn import model_selection from sklearn.linear_model import LogisticRegression from sklearn.neighbors import KNeighborsClassifier from sklearn.svm import SVC from sklearn.ensemble import RandomForestClassifier