Elastic Net Regression via Gradient Descent
Linear Models DS practice problem on Onlearn.
Difficulty: medium.
Topics: Understanding Elastic Net Regression via Gradient Descent, L1 Norm Penalty, L2 Norm Penalty, Elastic Net Mixing Parameter, Subgradient Descent, Multicollinearity Mitigation, Linear Algebra, Numerical Optimization, Statistical Learning Theory, Calculus, Computational Complexity, Gradient Descent Variants, Regularization Techniques, Matrix Factorization, Loss Function Design, Feature Selection Methods.
Implement Elastic Net Regression using gradient descent, combining L1 and L2 penalties to handle multicollinearity and encourage sparsity in the feature weights.