#25 Regularization Techniques | Introduction to Machine Learning (Tamil) 3.6

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This video talks about regularization techniques, specifically Ridge and Lasso regression, which are used to prevent overfitting in models by adding a penalty to the cost function.
- Ridge Regression: Also known as L2 regularization, it adds the square of the magnitude of coefficients as a penalty term to the cost function. This technique shrinks the coefficients, but never reduces them to zero, encouraging smaller but non-zero coefficients.
- Lasso Regression: Also known as L1 regularization, it adds the absolute value of the coefficients as a penalty, which can shrink some coefficients to zero, effectively performing feature selection by eliminating less important features.
These techniques help create more generalizable models by controlling complexity.
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Видео #25 Regularization Techniques | Introduction to Machine Learning (Tamil) 3.6 канала NPTEL-NOC IITM
Regularization, RidgeRegression, LassoRegression, L1Regularization, L2Regularization, Overfitting, AI, MathforML, DataScience, MLConcepts