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:
- What is Precision in Machine Learning ?
- What is Accuracy in Machine Learning ?
- How to compute Precision and Recall to evaluate the performance for our classifiers ?
- How to read the confusion matrix ?
- How to draw a confusion matrix ?
- Interpreting the confusion matrix that is given to us.
- What does the confusion matrix gives?
- What is ROC-AUC Curve and how it is used to distinguish the performance of classifiers ?
- 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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