20 Popular Machine Learning Metrics. Part 1: Classification & Regression Evaluation Metrics

Choosing the right metric is crucial while evaluating machine learning (ML) models. Various metrics are proposed to evaluate ML models in different applications, and I thought it may be helpful to provide a summary of popular metrics in a here, for better understanding of each metric and the applications they can be used for. In some applications looking at a single metric may not give you the whole picture of the problem you are solving, and you may want to use a subset of the metrics discussed in this post to have a concrete evaluation of your models. Here, I provide a summary of 20 metrics used for evaluating machine learning models. There is no need to mention that there are various other metrics used in some applications (FDR, FOR, hit@k, etc.), which I am skipping here.