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Optimization for Machine Learning with NumPy and SciPy

Solving Ridge Regression

Explore the process of solving ridge regression within convex optimization. Understand how regularization helps avoid overfitting when inputs are correlated, and learn to compute optimal parameters using gradient-based solutions. This lesson applies practical coding with NumPy to implement and visualize ridge regression outcomes compared to linear regression.

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Ridge regression

Ridge regression or L2L_2-regularized linear regression is a type of linear regression problem where model coefficients are constrained to zero or near zero. Given X∈RN×dX \in \R^{N \times d} that denotes the set of NN dd ...