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Confusion matrix for
CART
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Classification
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Find definitions and interpretations for every statistic in the Confusion matrix.
In This Topic
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
7 Node Classification Tree: Heart Disease versus Age, Rest Blood Pressure, Cholesterol, Max Heart Rate, Old Peak, Sex, Fasting Blood Sugar, Exercise Angina, Rest ECG, Slope, Thal, Chest Pain Type, Major Vessels
Optimal Tree: 7 terminal nodes, 6 internal nodes Max Tree: 21 terminal nodes, 20 internal nodes Confusion Matrix Predicted Class (Training) Predicted Class (Test) Actual Class Count Yes No %Correct Yes No %Correct Yes (Event) 139 117 22 84.2 105 34 75.5 No 164 22 142 86.6 24 140 85.4 All 303 139 164 85.5 129 174 80.9 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.
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