bagging and boosting stacking

from sklearn.ensemble import StackingClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
import warnings
warnings.filterwarnings("ignore")

# Train a stacking ensemble
estimators = [
    ('tree', DecisionTreeClassifier(random_state=42)),
    ('forest', RandomForestClassifier(n_estimators=100, random_state=42)),
    ('ada', AdaBoostClassifier(base_estimator=DecisionTreeClassifier(max_depth=1), n_estimators=100, random_state=42))
]
stack = StackingClassifier(estimators=estimators, final_estimator=LogisticRegression(random_state=42))
stack.fit(X_train, y_train)

# Make predictions
stack_pred = stack.predict(X_test)

# Calculate accuracy
stack_acc = accuracy_score(y_test, stack_pred)

print(f"Stacking Accuracy: {stack_acc:.2f}")