Unit Testing Machine Learning Code

ML systems combine ordinary software logic with statistical behavior. Unit tests are best suited to small components whose expected behavior can be specified: parsing a record, applying a scaler, encoding a category, computing a loss or formatting a prediction response. Tests should use compact fixtures with known values, including boundary cases such as missing fields, empty batches and unexpected categories. Data transformation tests can assert output shape, feature order, dtype and selected numerical values. These checks prevent silent changes in preprocessing from shifting model inputs. Loss-function tests can compare a result with a hand-computed example, check reductions such as mean versus sum, and exercise numerical edge cases. Model wrapper tests can verify that prediction dimensions match the request and that invalid inputs fail clearly. Pytest fixtures and parametrized tests help reuse cases without obscuring what each assertion means. A tiny-overfit test trains a small model on a tiny dataset and checks that the training objective decreases or reaches a chosen tolerance. This can reveal disconnected gradients, optimizer errors or label misalignment. It is not evidence of generalization: memorizing a few examples is expected. Keep such tests small and deterministic enough for CI, while separating them from longer integration or benchmark jobs. Randomness and hardware can make exact floating-point outputs unstable. Use tolerances for numerical comparisons, set seeds where appropriate and avoid brittle assertions on internal implementation details. Unit tests do not prove data quality, calibration, fairness or production performance. Add integration checks for serialization and loading, schema compatibility, resource behavior and end-to-end prediction. Model-quality gates need representative validation data and metrics. A useful test suite divides responsibilities: unit tests for code contracts, data checks for input expectations, and model evaluation for statistical behavior. This provides fast feedback without confusing software correctness with model usefulness.