Master the 6 Essential Regression Metrics for Machine Learning
This article presents six key regression evaluation metrics—RMSE, MAPE, MSE, MAE, R² Score, and Weighted MAPE—explaining their formulas, advantages, and limitations, and shows how each helps assess model performance in machine learning tasks.
The following six regression metrics are commonly used to evaluate machine‑learning models:
RMSE (Root Mean Square Error) : penalizes larger errors more heavily; expressed in the same unit as the target, making it easy to interpret.
MAPE (Mean Absolute Percentage Error) : shows average error as a percentage; can become unstable when actual values are close to zero.
MSE (Mean Square Error) : strongly penalizes large errors; highly sensitive to outliers.
MAE (Mean Absolute Error) : treats all errors equally; more robust to outliers than MSE.
R² Score (Coefficient of Determination) : measures the proportion of variance explained by the model; can be negative if the model performs worse than a baseline.
Weighted MAPE : a weighted version of MAPE that reduces bias when actual values are small.
These metrics are core tools for assessing the performance of regression models in machine‑learning projects.
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