When building predictive models, you obviously need to pay close attention to their performance. That is essentially what it is all about – getting the prediction right. Especially if you are working for paying clients you need to prove that the performance of your models is good enough for their business. Fortunately, there is a whole bunch of statistical metrics and tools at hand for assessing model’s performance.
In my experience, performance metrics for (especially binary) classification tasks such as confusion matrix and derived metrics are naturally understood by almost anyone. A bit more problematic is the situation for regression and time series. For example when you want to predict future sales or want to derive income from other parameters, you need to show how close your prediction is to the observed reality.
I will not write about (adjusted) R-squared, F-test and other statistical measures. Instead, I want to focus on performance metrics that should represent more intuitive concept of performance as I believe they can help you to sell your work much more. These are:
- mean absolute error
- median absolute deviation
- root mean squared error
- mean absolute percentage error
- mean percentage error