from mlxtend.classifier import StackingClassifier from sklearn.linear_model import LogisticRegression from sklearn.naive_bayes import GaussianNB from sklearn.ensemble import RandomForestClassifier from sklearn.datasets import load_iris from sklearn.model_selection import train_test_split # Create a dataset X, y = load_iris(return_X_y=True) # Split the dataset into training and test sets X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3) # Create a list of base classifiers lr = LogisticRegression() nb = GaussianNB() rf = RandomForestClassifier() lvl0_classifiers = [lr, nb, rf] # Create a meta-classifier meta_classifier = LogisticRegression() # Create a StackingClassifier stacking = StackingClassifier(classifiers=lvl0_classifiers, meta_classifier=meta_classifier) # Train the StackingClassifier stacking.fit(X_train, y_train) # Use the predict_proba function to estimate label probabilities probas = stacking.predict_proba(X_test) print(probas)In this example, we use the StackingClassifier with three base classifiers (Logistic Regression, Gaussian Naive Bayes, and Random Forest) and a meta-classifier (Logistic Regression). We then split the Iris dataset into training and test sets and train the StackingClassifier on the training data. Finally, we use the predict_proba function to estimate the label probabilities for the test data. The package library for mlxtend.classifier is scikit-learn (sklearn).