stacking.py

from sklearn.linear_model import LinearRegression

# Create a list of the tuned models with names
models = [
    ('xgb', xgb_tuner.best_estimator_),
    ('rf', rf_tuner.best_estimator_),
    ('ridge', ridge_tuner.best_estimator_)
]

# Create the stacking model
stacking_regressor = StackingRegressor(
    estimators=models,
    final_estimator=LinearRegression(),
    cv=5,
    n_jobs=-1
)

# Fit the stacking model on your training data
stacking_regressor.fit(X_train, y_train)