What are stacking techniques in machine learning?

What are stacking techniques in machine learning?

Stacking techniques involve combining multiple models to improve prediction accuracy. It uses a meta-learner to aggregate the predictions.

How does stacking differ from bagging and boosting?

While bagging and boosting focus on improving a single model's performance, stacking combines multiple models to leverage their strengths.

Can you give me an example of how to implement stacking in Python?

Sure! You can use libraries like scikit-learn to implement stacking. You would typically use the StackingClassifier or StackingRegressor.

What are some common models used in stacking?

Common models include decision trees, logistic regression, and support vector machines. You can mix different types for better results.