Black box models have the potential to deliver impressive results; however, they tend to be unreliable, difficult to debug, and problematic to deploy in regulated sectors. By using Explainable AI (XAI) tools such as SHAP and LIME, these models become transparent and hence more understandable, since one can then see the reasons that led to…
A machine learning model’s effectiveness is heavily reliant on how well its evaluation is conducted. Numerous “excellent” models fail in production because of inadequate data splitting and unnoticed data leakage. This article outlines ways to organize the train/validation/test splits and illustrates what data leakage looks like in actual projects. Why splits matter If you train…
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