from mlxtend.classifier import StackingClassifier from sklearn.linear_model import LogisticRegression from sklearn.naive_bayes import GaussianNB from sklearn.tree import DecisionTreeClassifier clf1 = DecisionTreeClassifier(random_state=1) clf2 = RandomForestClassifier(random_state=1) clf3 = LogisticRegression(random_state=1) clf4 = GaussianNB() meta_clf = LogisticRegression() stack_clf = StackingClassifier(classifiers=[clf1, clf2, clf3, clf4], meta_classifier=meta_clf) stack_clf.fit(X_train, y_train) y_pred = stack_clf.predict(X_test)In this example, four different classifiers (a decision tree classifier, a random forest classifier, logistic regression, and Gaussian Naive Bayes) are combined using a logistic regression model as the meta-classifier. The `fit()` function trains the stacking classifier on the training data (X_train and y_train), and the `predict()` function is used to generate predictions on the test data (X_test). The mlxtend.classifier package in Python is part of the mlxtend library, which provides tools for data preprocessing, feature selection, visualization, and more.