What are some effective strategies to manage bias and variance in machine learning?

What are some effective strategies to manage bias and variance in machine learning?

To manage bias and variance, you can use techniques like cross-validation, regularization, and ensemble methods.

Can you explain how regularization helps with bias and variance?

Regularization adds a penalty to the loss function, which helps prevent overfitting, thus reducing variance.

What is cross-validation and how does it work?

Cross-validation is a technique to assess how the results of a statistical analysis will generalize to an independent dataset.

Are there specific ensemble methods you recommend?

Yes, methods like bagging, boosting, and stacking are popular ensemble techniques that can help manage bias and variance.