Last updated: February 12, 2026 by the editorial team
Author's): Shahidullah Kawsar
Originally published in Towards Artificial Intelligence.
Machine Learning Interview Preparation, Part 23
Key performance indicators (KPIs) such as mean square error (MSE), root mean square error (RMSE), and mean absolute error (MAE) provide quantitative ways to measure how closely a model's predictions match actual values. Each metric captures error from a different perspective, emphasizing aspects such as sensitivity to outliers, interpretability, or scale. Understanding these KPIs is essential to effectively selecting, comparing, and tuning regression models. This blog discusses the most common regression error indicators.
The article details various key performance indicators (KPIs) such as mean squared error (MSE), mean absolute error (MAE) and coefficient of determination (R-squared), explaining their importance and application in evaluating regression models. Highlights the importance of understanding how each metric reflects model performance and the consequences of using metrics such as root mean square error (RMSLE) under specific conditions. The discussion also touched on common pitfalls of using R-squared and the benefits of adjusted metrics in more complex models, ultimately emphasizing the need to carefully select evaluation methods based on the context of the model.
Read the entire blog for free on Medium.
Published via Towards AI
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