What is a Confusion Matrix in Machine Learning?
A confusion matrix provides a summary of all prediction results of a classification problem. Each result is shown with its corresponding number of correct/incorrect predictions (see True Positive, True Negative, False Positive, False Negative), count values and classification criteria. By providing a neat summary of all possible results, the confusion matrix lets you know the ways your classification model could get confused when making the predictions. It helps identify errors and the type of errors made by the model and thus helps improve the accuracy of the classification model.