Stepwise Selection Made Simple: Improve Your Regression Models in Python

Reducing the number of variables in a regression model is a strategic choice to balance the bias-variance trade-off and improve predictive performance. The process involves deciding which variables to include to reduce dimensionality without losing critical information. Several scoring criteria are used to evaluate competing models, including Mallow's Cp statistic, which balances model fit against complexity. Other methods based on likelihood and penalization include the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC), which apply different penalties for model complexity. These techniques are illustrated using a Python application on the Communities and Crime dataset.