Interpreting Regression and Classification Performance Metrics (With complete Python Code)

Welcome to my 24th episode of the Engineering Exploration series!

If you’ve been following my journey, you know that I delve into diverse areas of engineering fields, offering tutorials, complete working codes, and deep insights. My passion for continuous learning has taken me from Mechanical Engineering to explore topics in Material Science, Computer Engineering, Data Science, Finite Element Analysis, Computer-Aided Design, Machine learning, Deep Learning, and Renewable Energy systems. Through these articles, my goal is to empower the next generation of engineers and scientists, contributing to society by sharing knowledge and igniting curiosity.

In this article, we’ll explore the key metrics used to evaluate the performance of supervised Machine Learning (ML) and Deep Learning (DL) models. Broadly, supervised machine learning algorithms can be classified into Regression and Classification algorithms. Understanding these metrics is crucial for assessing how well a model performs, whether you’re dealing with regression, classification, or clustering problems. I’ll walk you through twelve (6 regression and 6 classification) of the most commonly used metrics, explaining when and why to use each one, depending on your task, data, and objectives. Whether you’re refining a model or selecting the best one for your needs, these insights will guide your decision-making process.

The article has been moved to the following Medium platform for a wide readership.

Classification Metrics: https://medium.com/@thebinayak/classification-metrics-in-machine-learning-complete-python-code-visualizations-8669b6314b60

Regression ML Metrics: https://medium.com/@thebinayak/interpreting-regression-ml-models-performance-metrics-with-complete-python-code-e70db23adefc