A practical applications guide to machine learning regression models in psychology with Python

Abstract

This guide presents a detailed overview of the most used machine learning (ML) techniques for psychologists who may not be familiar with advanced statistical methods, algorithms, or programming. Recognizing the growing interest in using data-driven approaches within psychological research, this guide describes applying ML techniques to investigate complex psychological phenomena. The paper covers the spectrum of algorithms, including decision trees, random forests, gradient boosting, stochastic gradient boosting, and XGBoost, highlighting their concepts and practical applications in psychology. Aiming to bridge the gap between theoretical understanding and practical performance, this paper offers step-by-step instructions on data preprocessing, correlation exploration, feature selection, and model evaluation within the Python programming environment. Readers are offered the necessary tools to apply ML in their research through explanations, examples, and visualization.