Introduction

Introduction

Metrics tell you how much a model gets wrong. Error analysis tells you what it gets wrong and why — which is what you need to know to improve it.

It's like a teacher marking exams. Knowing a student scored 70% is useful, but knowing they always mix up fractions and decimals tells you exactly what to teach next. Professional ML engineers spend a lot of time looking at mistakes, because that's where the next improvement idea usually comes from.

This topic shows two practical techniques:

1. turning the confusion matrix into a heat map to spot patterns;
2. looking at individual misclassified examples to understand them.