How to Test Machine Learning Code and Systems
Eugene Yan
The article describes testing methodologies for machine learning systems, distinguishing between pre-train tests that verify implementation correctness and post-train tests that check learned behavior. A practical example demonstrates testing a decision tree classifier on the Titanic dataset (~900 rows) using checks like output range validation (0 to 1), invariance tests, and directional expectation tests such as females having higher survival probability than males. The testing workflow ensures both correct written logic before training and expected learned patterns after training through unit tests, code coverage analysis, and behavioral validation inspired by NLP testing frameworks.
Why it matters
Checking for correct implementation, expected learned behaviour, and satisfactory performance.
