Data Science, Machine Learning, Data Analytics, Knowledgebase Analytics, Regularization.
squares loss, while the second term is the L2 penalty. Use Cases: Ridge regression is particularly beneficial when features are correlated, leading to multicollinearity issues. It can produce stable coefficients, even when predictors are highly interrelated. For datasets where the number of