Lecture-30: LASSO Regression (L1 regularization )
-This lecture about LASSO regression with L1 regularization.
-Performs L1 regularization, i.e., adds penalty equivalent to the absolute value of the magnitude of coefficients in the optimization objective.
- This penalty allows some coefficient values to go to the value of zero, allowing input variables to be effectively removed from the model, providing a type of automatic feature selection.
-Along with shrinking coefficients, LASSO perform feature selection. Some of the coefficients exactly zero.
-Lecture-28: Linear Regression & Regression Data Representation
• Lecture-28: Linear Regression & Regression...
-Lecture-29: Ridge Regression (L2 Regularization)
• Lecture-29: Ridge Regression (L2 Regulariz...
Lecture-4: Gradient Descent Algorithm in Machine Learning
• Lecture-4: Gradient Descent Algorithm in M...