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Predictive models are the backbone of modern data analysis, particularly in the context of machine learning and artificial intelligence. To assess the quality of a predictive model, various metrics are used to accurately evaluate its effectiveness. In this post, we will discuss key metrics such as accuracy, sensitivity, specificity, precision (PPV), negative predictive value (NPV), F1-score, and others. Each of these metrics has its specific applications and limitations, which should be considered when interpreting the results.

Use case with the BPI’12 dataset