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3. Confusion Matrix

True Positives (TP):
- Predicted YES, and it turns out correct as a YES
True Negatives (TN):
- Predicted NO, and it turns out correct as a NO
False Positives (FP):
- Predicted YES, but it turns out to be a NO. (Also known as a "Type I error.")
False Negatives (FN):
- Predicted NO, but it turns out to be a YES. (Also known as a "Type II error.")

Confusion Matrix Rates
1. Accuracy (Overall, how often is the classifier correct?):
  - (TP+TN)/(TP+TN+FP+FN)
2. Misclassification Rate or Error Rate (Overall, how often is the classifier wrong?):
  - (FP+FN)/(TP+TN+FP+FN) {or equivalent to 1 minus Accuracy}
3. Sensitivity, Recall, or True Positive Rate (When it's actually YES, how often does it predict YES?):
  - TP/(TP+FN)
4. Specificity (When it's actually NO, how often does it predict NO?):
  - TN/(TN+FP)
5. Precision (When it predicts YES, how often is it correct?):
  - TP/(TP+FP)
6. Prevalence (How often does the YES condition actually occur in our sample?):
  - (TP+FN)/(TP+TN+FP+FN)

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