ML models have a significant impact on our lives, they are involved in sensitive issues such fraud detection, autonomous driving and deciding which content will be displayed to millions of users 24 hours a day. Despite the large impact they have, ML models are often not evaluated thoroughly and are deployed without a proper understanding of their capabilities and limitations.

This post focuses on some tips and insights regarding proper evaluation of machine learning models and raises some common misconceptions in the field.

Accuracy Is Not Everything

Perhaps the most common metric used to measure the performance of classification models is machine learning model accuracy.

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