Introduction

This article is a brief introduction to some of the ideas behind measuring the accuracy of supervised machine learning tools.  The first half deals with classification algorithms; those that decide which of several classes a sample belongs to. The chief measure of accuracy there is the confusion matrix, from which a whole host of other statistics may be extracted.  The second half of this article deals with regression algorithms, which give a predicted numerical value for some outputs from known inputs.