What is the Confusion Matrix?

• A confusion matrix is a table that is often used to describe the performance of a classification model (or “classifier”) on a set of test data for which the true values are known.

• It allows the visualization of the performance of an algorithm.

• A confusion matrix is a summary of prediction results on a classification problem.

• The number of correct and incorrect predictions is summarized with count values and broken down by each class.

• This is the key to the confusion matrix.

• It gives us insight not only into the errors being made by a classifier but, more importantly, the types of errors that are being made.

Here,

• Class 1: Positive

• Class 2: Negative

Definition of the Terms:

1. Positive (P): Observation is positive

2. Negative (N): Observation is not positive

3. True Positive (TP): Observation is positive, and is predicted to be positive.

4. False Negative (FN): Observation is positive, but is predicted negative.

5. True Negative (TN): Observation is negative, and is predicted to be negative.

6. False Positive (FP): Observation is negative, but is predicted positive.