Ridge regression

Related lectures (29)

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Compares L1 and L0 penalization in linear regression with orthogonal designs using greedy algorithms and empirical comparisons.

Covers analysis of variance, model building, variable selection, and function estimation in regression methods.

Covers Support Vector Regression principles, optimization, and hyperparameters' influence on the fit.

Explores Tikhonov regularization, also known as Ridge regression, and its application to polynomial regression.

Explores practical aspects of the Gaussian linear model, focusing on variable selection and regularization methods.

Explores kernel methods in machine learning, emphasizing their application in regression tasks and the prevention of overfitting.

Covers the concept of gradient descent, a universal algorithm used to find the minimum of a function.

Covers penalization in ridge regression, emphasizing the trade-off between bias and variance in regression models.