Table of contents

💡 Topics

In this lesson, we will cover the following:

• Accuracy
• Class imbalance
• Measuring what is a good classifier
• True positives, false negatives, false positives and true negatives
• Decision boundaries
• Confusion matrices
• Different types of cost functions
• ROC Curves and the area under the curve
• Precision and recall
• $F_{\beta}$ and $F_{1}$ scores

🎓 Intended learning outcomes

At the end of this lesson, the student is expected to:

• know that not all performance measures are applicable in all situations
• know that a single number cannot capture all aspects of decision-making performance