About this document
Understanding Regularization in ML by Pallab Puri is a document available to read on EtoBox.
Regularization helps address the problem of overfitting in machine learning models. It works by adding a penalty term to the cost function that shrinks the magnitude of parameters θ. This has the effect of preferring simpler models with fewer large parameters. Regularization can be applied to linear regression, logistic regression, and other models by modifying the cost function and gradient descent update rules to include the regularization term. The regularization parameter λ controls the tradeoff between
- Author
- Pallab Puri
- Language
- EN