Lasso and Ridge Regression

Описание к видео Lasso and Ridge Regression

This video explains two important regularization techniques: Lasso and Ridge Regression. These methods help prevent overfitting by adding penalties to large coefficients, improving model performance. The key difference between them lies in how they treat the coefficients: Lasso shrinks some coefficients to zero, promoting sparsity, while Ridge reduces the magnitude of all coefficients but keeps them non-zero. Ideal for those interested in understanding how these techniques are applied in machine learning.

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