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Optimization for Machine Learning with NumPy and SciPy

Solving Ridge Regression

Explore how ridge regression addresses overfitting in linear models by adding L2 regularization. Learn to compute the optimal parameters using gradient-solving techniques and visualize the results with Python's NumPy, improving prediction accuracy when inputs are correlated.

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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-dimensional inputs and their corresponding true labels ...