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Regularisation in Machine Learning

Talla Veerendranath
19 May 0 0


In Machine Learning, Regularization is the concept of shrinking or regularizing the coefficients towards zero.

It helps the model to prevent overfitting.

Overfitting in Machine Learning is referred to as an algorithm while getting model has a lot of features or a low number of observations. 

A linear model tends to overfit and when an algorithm gets modelled has a lot of features or a low number of observations, at that time the variable selection becomes tricky.

In Machine Learning, Regularization is achieved by the help of:

1. Lasso Regression (L1)

2. Ridge Regression (L2)

3. Elastic Net Regression (L1+L2).

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