What is regularization? What are some of its techniques?
Regularization is used to reduce over-fitting in machine learning models. It helps the models to generalize well and make them robust to outliers and noise in the data.
There are mainly two types of regularization,
- L1 Regularization (Lasso regularization) - Adds the sum of absolute values of the coefficients to the cost function.
- L2 Regularization (Ridge regularization) - Adds the sum of squares of coefficients to the cost function.
- Where determines the amount of regularization.