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Which is more important to you: model accuracy or model performance?

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Such machine learning interview questions tests your grasp of the nuances of machine learning model performance! Machine learning interview questions often look towards the details. 

There are models with higher accuracy that can perform worse in predictive power—how does that make sense?

Well, it has everything to do with how model accuracy is only a subset of model performance, and at that, a sometimes misleading one. For example, if you wanted to detect fraud in a massive dataset with a sample of millions, a more accurate model would most likely predict no fraud at all if only a vast minority of cases were fraud. However, this would be useless for a predictive model—a model designed to find fraud that asserted there was no fraud at all! Questions like this help you demonstrate that you understand model accuracy isn’t the be-all and end-all of model performance.

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Model accuracy is a subset of model performance. The accuracy of the model is directly proportional to the performance of the model. Thus, better the performance of the model, more accurate are the predictions.

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