Role of Confusion matrix in machine learning

Why to read this?

A confusion matrix is a summary of prediction results on a classification problem. The number of correct and incorrect predictions are summarised with count values and broken down by each class. 

Technical explanation

Each row of the matrix represents the instances in a predicted class, while each column represents the instances in an actual class (or vice versa).[8] In abstract terms, the confusion matrix is as follows:

Purpose

A much better way to evaluate the performance of a classifier is to look at the confusion matrix. The general idea is to count the number of times instances of class A are classified as class B (Refer below example). Using this, we can identify accuracy, misclassification rate etc. 

Reference

Thanks to these helping hands

https://en.wikipedia.org/wiki/Confusion_matrix

https://www.dataschool.io/simple-guide-to-confusion-matrix-terminology/

https://machinelearningmastery.com/confusion-matrix-machine-learning/