Confusion Matrix is a table that describes the performance of a classification model on a set of test data for which the true values are known. This type of visualization is intuitive and simple to understand
A confusion matrix, also known as an error matrix, is a specific table layout that allows visualization of the performance of a model. Each column of the table represents the count of the instances in a predicted class while each row represents the count of the instances in an actual class (or vice versa). This is a simple and intuitive way to summarize and visualize the model performance. where:
TP = True Positive; FP = False Positive;
TN = True Negative; FN = False Negative
True/False refers to the correctness of the prediction
Positive/Negative refers to the prediction itself.