202511171356 Status: idea Tags: Datascience, Machine Learning
Confusion Matrix
A confusion matrix, as the name suggests, is a matrix of numbers that tell us where a model gets confused. It is a class-wise distribution of the predictive performance of a classification model—that is, the confusion matrix is an organized way of mapping the predictions to the original classes to which the data belong.
This also implies that confusion matrixes can only be used when the output distribution is known, i.e., in Supervised learning frameworks.
The confusion matrix not only allows the calculation of the accuracy of a classifier, be it the global or the class-wise accuracy, but also helps compute other important metrics that developers often use to evaluate their models.