“Doubts are good. Confusion is excellent. Questions are awesome.
All these are attempts to expand the wisdom of mind.”
_― _Manoj Arora
In a classification problem, it is often important to specify the performance assessment. This can be valuable when the cost of different misclassifications varies significantly. Classification accuracy is also a measure showing how well the classifier correctly identifies the objects.
A confusion matrix also called a contingency table or error matrix gets across the picture when it comes to visualizing the performance of a classifier. The columns of the matrix represent the instances of the predicted classes and the rows represent the instances of the actual class. (Note: It can be the other way around as well.)
The confusion matrix shows the ways in which your classification model is confused when it makes predictions.
#machine-learning #data-science #confusion-matrix #hypothesis-testing #python