Regression Analysis with Scikit-Learn (part 1 - Linear)

2022/07/13 by Matthew J. Lavin · 1 voice
Computer Science · Engineering · #Advanced Data Processing Techniques #Computational Physics and Python Applications

paper · doi:10.46430/phen0099

openalex publication_date 2022/07/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/26

Abstract

This lesson is the first of a two-part lesson focusing on an indispensable set of data analysis methods, logistic and linear regression. It provides an overview of linear regression and walks through running both algorithms in Python (using scikit-learn). The lesson also discusses interpreting the results of a regression model and some common pitfalls to avoid.

Discussions

• Learn how to use linear regression with Matthew J. Lavin: doi.org/10.46430/phe... [bsky, 1 points, 0 comments]

Related

• A Tutorial on Regression Analysis: From Linear Models to Deep Learning -- Lecture Notes on Artificial Intelligence
• Linear regression in the Bayesian framework
• Fundamentals of Regression
• RuleKit 2: Faster and simpler rule learning
• Regression Modeling Strategies: With Applications to Linear Models, Logistic Regression, and Survival Analysis