- 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
- http://www.ecst.csuchico.edu/~juliano/csci298/DataMining/Slides/Witten+Frank/2e/HTML/chapter_5/img15.html

Advertisements