- 1. Evaluation Metrics
- Confusion Matrix – predicted class, actual class, accuracy..
- Cost Matrix – Just multiply and add corresponded item in the original cost matrix model.
- Cost-sensitive Measures – Precision and Recall
Precision: what % of tuples that the classifier labeled as positive are actually positive
Recall: what % of positive tuples did the classifier label as positive
F measure – just a harmonic mean of precision and recall. “a measure of a test’s accuracy” (Wikipedia)
- 2. Model Evaluation