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Evaluating AI Classifiers

Use a confusion matrix, precision and recall to evaluate a classifier against a simple baseline.

3 reading lessons · about 30 min ·written by MTT

The idea

A baseline is a simple comparison method, such as predicting the most common class. Keep evaluation examples separate from training and threshold tuning. Duplicate or future-derived information can leak into the test and inflate scores. Accuracy can be misleading when one class is rare, so record the class distribution and the kinds of mistakes that matter.

Worked example

Only 10 of 100 fictional messages are urgent. A system that labels everything non-urgent has 90% accuracy but misses every urgent message. That baseline reveals why an accuracy-only target is insufficient. Use labeled examples not used to develop the classifier to assess whether a new system improves the actual decision.

Try it

Create ten fictional messages with two marked urgent. Calculate the accuracy of always predicting non-urgent and count missed urgent cases. Define a separate development and test set. Identify one way that copying the same example into both sets would distort the evaluation.

Lesson 1 of 3 · About 10 min

Choose a baseline and a test set

Check your understanding

Course quiz

Finish the course to unlock the quiz

Complete all 3 lessons and 5 questions open up here. You have 3 to go.

What you will learn

  • Build a binary confusion matrix
  • Calculate precision and recall
  • Choose metrics based on the cost of mistakes

Before you start

Prerequisites
Basic percentages and familiarity with classification. A calculator or spreadsheet is sufficient.
Cost
Free introductory reading lessons, exercises and quiz. Optional third-party tools, hosting or AI subscriptions may cost money.

Original introductory lessons and assessment by Master the Trick. Estimated times include the suggested exercises.