Module 5 of 6Lesson 1 of 2~16 min
Read classification metrics
How to read a confusion matrix and pick the metric that truly reflects what a classifier's errors cost. You avoid approving a model on a flattering overall figure that hides a poorly handled critical category, and you speak the same language as the data team.
Lesson objective
By the end of this lesson, you will be able to read a confusion matrix, choose between accuracy, precision, recall and F1 based on the cost of errors, and spot when class imbalance makes a score misleading.
Questions this lesson answers
- How do you read a confusion matrix for an AI classifier?
- Why can a high accuracy score hide a classification problem?
- When does recall matter more than precision for an AI classifier?
Topics covered
- confusion matrix
- precision and recall
- F1 score
- class imbalance
- classification metrics
Where it fits
Choose the metrics
Which metrics should you read, and what do they say about the stage to fix?
Lessons in this module
- Read classification metrics (this lesson)
- Read RAG metrics
What you will learn in this module
This lesson is part of the course Evaluate an AI feature: test sets, metrics and LLM judges
- Choose and interpret the right metrics for a classification and for a RAG system, and infer which stage to fix.
Related courses
- AI & ProductAdvancedRegular practice: you already use the tools or work on this topic.
Build an AI assistant for your product
3 hr
- AI & ProductAdvancedRegular practice: you already use the tools or work on this topic.
Design a RAG architecture that fits your product
3 hr 30 min
- AI & ProductExpertAdvanced level: for deeper mastery and specialization.
Ship and monitor an AI feature in production
3 hr