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Making sense of the confusion matrix

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-guid... Evaluating a classifier with scikit-learn (video):    • How to evaluate a classifier in sciki...   ROC curves and AUC explained (video):    • ROC Curves and Area Under the Curve (...   == DATA SCHOOL INSIDERS == Join "Data School Insiders" on Patreon for bonus content:   / dataschool   == WANT TO GET BETTER AT MACHINE LEARNING? == 1) WATCH my scikit-learn video series:    • Machine learning in Python with sciki...   2) SUBSCRIBE for more videos: https://www.youtube.com/dataschool?su... 3) ENROLL in my Machine Learning course: https://www.dataschool.io/learn/ 4) LET'S CONNECT! - Newsletter: https://www.dataschool.io/subscribe/ - Twitter:   / justmarkham   - Facebook:   / datascienceschool   - LinkedIn:   / justmarkham