How do you interpret a confusion matrix? How can it help you to evaluate your machine learning model? What rates can you calculate from a confusion matrix, and what do they actually mean?

In this video, I'll start by explaining how to interpret a confusion matrix for a binary classifier:
0:49 What is a confusion matrix?
2:14 An example confusion matrix
5:13 Basic terminology

Then, I'll walk through the calculations for some common rates:
11:20 Accuracy
11:56 Misclassification Rate / Error Rate
13:20 True Positive Rate / Sensitivity / Recall
14:19 False Positive Rate
14:54 True Negative Rate / Specificity
15:58 Precision

Finally, I'll conclude with more advanced topics:
19:10 How to calculate precision and recall for multi-class problems
24:17 How to analyze a 10-class confusion matrix
28:26 How to choose the right evaluation metric for your problem
31:31 Why accuracy is often a misleading metric


== RELATED RESOURCES ==

My confusion matrix blog post:
https://www.dataschool.io/simple-guide-to-confusion-matrix-terminology/

Evaluating a classifier with scikit-learn (video):
https://www.youtube.com/watch?v=85dtiMz9tSo&list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A&index=9

ROC curves and AUC explained (video):
https://www.youtube.com/watch?v=OAl6eAyP-yo


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