A confusion matrix is a table that is often used to describe the

A confusion matrix is a table that is often used to describe the performance of a artificial
intelligence classification model on a set of test data for which the true values are known.
In the field of machine learning and specifically the problem of statistical classification, a
confusion matrix, also known as an error matrix.
It allows easy identification of confusion between classes e.g. one class is commonly mislabeled
as the other. Most performance measures are computed from the confusion matrix.
A confusion matrix is a summary of prediction results on a classification problem.
The number of correct and incorrect predictions are summarized with count values and broken
down by each class. This is the key to the confusion matrix.
The confusion matrix shows the ways in which your classification model is confused when it
makes predictions.
Here,
• Class 1 : Positive
• Class 2 : Negative
• Positive (P) : Observation is positive (for example: is an apple).
• Negative (N) : Observation is not positive (for example: is not an apple).
• True Positive (TP) : Observation is positive, and is predicted to be positive.
• False Negative (FN) : Observation is positive, but is predicted negative.
• True Negative (TN) : Observation is negative, and is predicted to be negative.
• False Positive (FP) : Observation is negative, but is predicted positive.
Accuracy is given by the relation: