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LASSO Regression

Explore LASSO regression as a sparse linear regression technique that uses L1 regularization to minimize weights and select key features. Understand how to implement LASSO and cross-validated LassoCV in scikit-learn to build models that effectively reduce feature redundancy and enhance model simplicity.

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Chapter Goals:

  • Learn about sparse linear regression via LASSO

A. Sparse regularization

While ridge regularization uses an L2 norm penalty term, another regularization method called LASSO uses an L1 norm for the weights penalty term. Specifically, LASSO regularization will find the optimal weights to minimize the following quantity:

α∣∣w∣∣1+∑i=1n(xi⋅w−yi)2\alpha ||w||_1 + \sum_{i = 1}^n (\mathbf{x}_i \cdot w - y_i)^2 ...