A very short introduction to Ridge and Lasso regression

Regularization helps avoid overfitting of a machine learning model. Ridge and Lasso can be best explained with MLR (Multiple Linear Regression) in mind.

In PCA, when try to reduce the number of features. But in Ridge and Lasso regression, we try reduce the magnitude of the coefficients. By shrinking the coefficients, Ridge and Lasso regression reduce the model complexity and multi-collinearity, thus help in avoiding overfitting .That’s why they come under the category of shrinkage methods.



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