Predictive modeling with Machine Learning in R — Part 5 (Evaluation Metrics for Regression)

This is the fifth post in the series Predictive modeling with Machine Learning (ML)in R. For earlier posts, please refer to below.

Predictive modeling with Machine Learning in R — Part 1(Introduction)

Predictive modeling with Machine Learning in R — Part 2 (Evaluation Metrics for Classification)

Predictive modeling with Machine Learning in R — Part 3 (Classification — Basic)

Predictive modeling with Machine Learning in R — Part 4 (Classification — Advanced)

More data beats clever algorithms, but better data beats more data — Andrew Ng

Introduction

Welcome to the fifth installment of our series on predictive modeling with Machine Learning (ML) in R. In this post, we delve into the realm of regression analysis, focusing on the key metrics used to evaluate the performance of regression models. Understanding these metrics is crucial for developing models that accurately predict continuous outcomes, a common task in many fields such as finance, healthcare, and environmental science. Let’s explore some of the most widely used evaluation metrics for regression: Mean Squared Error (MSE), Root Mean Squared Error (RMSE), R² Score (Coefficient of Determination), and Mean Absolute Percentage Error (MAPE).