Regularized Linear Regression Models: Using Ridge Regression to Overcome Drawbacks of Ordinary Least Squares (OLS)

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Published in Towards Data Science, this article, part two of a three-part series, explores the concepts of model error modeling, model overfitting/underfitting, statistical shrinkage for bias reduction, and Ridge Regression (Tikhonov Regularization)

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