Evaluating Predictive Models in Practice: Key Metrics for Materials R&D MAE, RMSE, R2 and Beyond
In data-driven materials R&D, selecting the right machine learning model is not just about looking at numbers. While many tools automatically calculate a wide range of metrics, it is neither necessary nor effective to check them all. What truly matters is this: What do you want to prioritize when selecting your model? Instead of memorizing individual metrics in isolation, this article introduces a practical framework that groups them into **four key evaluation perspective. By structuring your thinking this way, you can make consistent and confident decisions—regardless of the tool you use.
