Confusion matrix for CART^® Classification

Find definitions and interpretations for every statistic in the Confusion matrix.

The Confusion matrix shows how well the tree separates the classes correctly using these metrics:

True positive rate (TPR) — the probability that an event case is predicted correctly
False positive rate (FPR) — the probability that a nonevent case is predicted incorrectly
False negative rate (FNR) — the probability that an event case is predicted incorrectly
True negative rate (TNR) — the probability that a nonevent case is predicted correctly

Interpretation

Statistics	Training (%)	Test (%)
True positive rate (sensitivity or power)	84.2	75.5
False positive rate (type I error)	13.4	14.6
False negative rate (type II error)	15.8	24.5
True negative rate (specificity)	86.6	85.4

In this example, the total number of Yes events is 139, and the total number of No events is 164.

In the training data, the number of predicted Yes events is 117, which is 84.2% correct.
In the training data, the number of predicted No events is 142, which is 86.6% correct.
In the test data, the number of predicted Yes events is 105, which is 75.5% correct.
In the test data, the number of predicted No events is 140, which is 80.9% correct.

Overall, the %Correct for the Training data is 85.5% and 80.9% for the Test data.

True positive rate (TPR) — 84.2% for the Training data and 75.5% for the Test data.
False positive rate (FPR) — 13.4% for the Training data and 14.6% for the Test data.