Regression
Everything on SkillVeris tagged Regression — collected across the glossary, study notes, blog, and cheat sheets.
6 resources across 2 libraries
Study Notes(5)
Evaluating Regression Models
Surveys the core metrics for assessing regression quality — MAE, MSE, RMSE, and R-squared — and explains when each is the right lens for model performance.
Linear Regression Explained
Understand how linear regression fits a straight-line relationship between features and a continuous target, and how it is trained and evaluated.
Logistic Regression Explained
Introduces logistic regression as a classification algorithm that models class probability via the sigmoid function, despite its regression-sounding name.
Polynomial and Multiple Regression
Extends simple linear regression to multiple predictors and curved relationships, showing how feature expansion lets a linear model fit non-linear patterns.
Regularization: Ridge and Lasso
Explains how L2 (ridge) and L1 (lasso) penalties shrink regression coefficients to combat overfitting, and why lasso can perform feature selection.