Author(s): Shahidullah Kawsar

Originally published on Towards AI.

Machine Learning Interview Preparation Part 23

Key Performance Indicators (KPIs) such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE) provide quantitative ways to measure how closely model predictions align with actual values. Each metric captures error from a different perspective, emphasizing aspects like sensitivity to outliers, interpretability, or scale. Understanding these KPIs is essential for selecting, comparing, and tuning regression models effectively. This blog explores the most common regression error metrics.

The article delves into various regression Key Performance Indicators (KPIs) such as Mean Squared Error (MSE), Mean Absolute Error (MAE), and the Coefficient of Determination (R-squared), explaining their significance and usage in evaluating regression models. It highlights the importance of understanding how each metric reflects model performance and the implications of using metrics like Root Mean Squared Logarithmic Error (RMSLE) under specific conditions. The discussion also includes common pitfalls of using R-squared and the advantages of adjusted metrics in more complex models, ultimately emphasizing the need for careful selection of evaluation methods based on the model context.

Read the full blog for free on Medium.

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Published via Towards AI