Lecture-34: LASSO Regression (L1-Regularization) Algorithm in python
-In this Lecture video, I have talked about how you can implement the LASSO Regression algorithm in python.
-It is generally used when we have more number of features because it automatically does feature selection.
-Lasso: Along with shrinking coefficients, lasso performs feature selection as well.
It shrinks the regression coefficients toward zero by penalizing the regression model with a penalty term called L1-norm, which is the sum of the absolute coefficients.
-LASSO selects only some feature while reduces the coefficients of others to zero. This property is known as feature selection and is absent in the case of the ridge.
Lecture-30: LASSO Regression (L1 regularization )-THEORY
• Lecture-30: LASSO Regression (L1 regu...