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

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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...