Evaluating a machine learning model is as much important as building it, if not more. The only way by which we can know that our model draws out accurate conclusions is by evaluating its performance by passing it through a number of criterion and analyzing the results obtained.
This article aims at providing the readers, a brief idea about the various performance evaluation metrics used. However, before moving on to the main article, let’s have a look at the basic terms and definitions that you’ll need to know so that you can understand this article better.
A true positive is…
Electronics and communications engineering student | Avid reader