Picking Examples to Understand Machine Learning Model - KDnuggets

Evaluating model relevance does not stop at measuring its performance. For many reasons it is important to know how it ended up making such predictions. A machine learning model can be explained using local explainability or global explainability. In this article, we will use a complementary approach by combining explainability and sample picking. Sample picking is a process with great added value to better understand models, their strengths and weaknesses.