Principal Components Regression

An Overview and scikit-learn Example

Principal components analysis (PCA) is a common and popular technique for deriving a low-dimensional set of features from a large set of variables. For more information on PCA, please refer to my earlier post on the technique. In this post, I’ll explore using PCA as a dimension reduction technique for... [Read More]

Regularization via Ridge Regression and the Lasso #1

An Introduction and Overview

Regularization is a method of fitting a model containing all predictors $p$ that regularize the coefficient estimates towards zero. Also known as constraining or shrinking the model’s coefficient estimates, regularization can significantly reduce the model’s variance and thus improve test error estimates and model performance. The two most commonly used... [Read More]