Performance metrics
In the previous chapter, where we talked about the predictive modeling process, we delved into the importance of assessing a trained model's performance using training and test datasets. In this section, we will look at specific measures of performance that we will frequently encounter when describing the predictive accuracy of different models. It turns out that depending on the class of the problem, we will need to use slightly different ways of assessing (the model's) performance. As we focus on supervised models in this book, we will look at how to assess regression models and classification models. For classification models, we will also discuss some additional metrics used for the binary classification task, which is a very important and frequently encountered type of problem.
Note
Note: In statistics, the term performance is usually interchangeable with accuracy.
Assessing regression models
In a regression scenario, let's recall that through our model...
