Lasso Regression Explained Simply | Machine Learning Basics
Lasso Regression is a regularized version of Linear Regression that can reduce overfitting and perform feature selection.
In this video, we explain Lasso Regression in a simple way:
what Lasso Regression is
how it relates to Linear Regression
how it reduces overfitting
why it can shrink coefficients to zero
how Lasso performs feature selection
what L1 regularization means
how Lasso differs from Ridge Regression
Ridge usually shrinks coefficients while keeping all features. Lasso can shrink some coefficients all the way to zero, which means it can remove less useful features from the model.
In the next video, we’ll continue with ElasticNet.
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