Accuracy and precision

Correctly evaluating model performance is a crucial task while working with machine learning. There are quite a few metrics that we may use to do so. That can be problematic for someone who just started the journey in this field — at least, it was for me. I will start with describing concepts like true/false negatives/positives as they are the base for more complex metrics. Then I will mention and explain metrics like accuracy, precision, recall, or calibration error. I will also explain the basics behind the confusion…