What are the advantages of ensemble learning over single models in Python?

What are the advantages of ensemble learning over single models in Python?

Ensemble learning combines multiple models to improve accuracy and robustness. It reduces overfitting and enhances generalization.

Can you give me examples of ensemble methods?

Sure! Common ensemble methods include Bagging, Boosting, and Stacking. Random Forest is a popular example of Bagging.

How does boosting work in ensemble learning?

Boosting works by sequentially training models, where each new model focuses on the errors made by the previous ones, improving overall performance.

Are there situations where single models perform better?

Yes, single models can perform better in simpler problems or when interpretability is crucial. They are also less computationally intensive.

What libraries in Python can I use for ensemble learning?

You can use libraries like Scikit-learn, XGBoost, and LightGBM for implementing ensemble learning techniques in Python.