This document discusses model evaluation and validation metrics for machine learning models. It introduces accuracy and its limitations, then covers alternative evaluation metrics like false positives, false negatives, F1 score, and ROC curves. These metrics are important for determining how well a model is performing and identifying errors to improve the model.

1. 01
Machine Learning Course

2. Model Evaluation and Validation
• how well is my model doing? [is this model good or not?]

5. Evaluation Metrics

12. When accuracy won't work
Can you help me think of a model that has
over 99 percent accuracy?

13. QUESTION 1 OF 2
• In the medical example, what is worse, a False
Positive, or a False Negative?
False Negative

14. QUESTION 2 OF 2
In the spam detector example, what is worse, a
False Positive, or a False Negative?
False positive

19. F1

20. F1 score

21. F-beta Score
Note that in the formula for Fβ​ score, if we set β=0, we get

23. ROC