What are the best practices for ensemble learning in Python?

What are the best practices for ensemble learning in Python?

Some best practices include using diverse models, tuning hyperparameters, and validating with cross-validation.

Can you explain why model diversity is important?

Model diversity helps to reduce overfitting and improves generalization by combining different perspectives on the data.

What are some common ensemble methods I should know about?

Common methods include bagging, boosting, and stacking. Each has its own strengths depending on the problem.

How do I implement a random forest in Python?

You can use the RandomForestClassifier from the sklearn.ensemble module. Just fit it to your training data!