Precision, Recall, Confusion Matrix and F1-Score | Performance Evaluation Metrics for Classification

In this video, we will learn about the performance evaluation metrics for classification models namely accuracy, confusion matrix and the ROC-AUC Curve (Receiver Operating Characteristic. We will first understand each of these metrics in detail:

1. What is Precision in Machine Learning ?
2. What is Accuracy in Machine Learning ?
3. How to compute Precision and Recall to evaluate the performance for our classifiers ?
4. How to read the confusion matrix ?
5. How to draw a confusion matrix ?
6. Interpreting the confusion matrix that is given to us.
7. What does the confusion matrix gives?
8. What is ROC-AUC Curve and how it is used to distinguish the performance of classifiers ?
9. How to use ROC-AUC Curve to determine which classifier is the best classifier and which classifier is the worst one ? and more...

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