docs/BENCHMARKS.md

Reproducible smoke benchmarks

These results are correctness-oriented smoke benchmarks, not claims about production accuracy. They were measured on a local Windows CPU with Python 3.13.1, RANDOM_STATE=42, OPTUNA_TRIALS=0, and MAX_CRITIC_RETRIES=0.

Dataset	Rows	Task	Selected model	Training CV score	Untouched test score	Wall time
scikit-learn Iris	150	multiclass classification	Logistic Regression	0.9583 weighted F1	0.9333 weighted F1	4.29 s
scikit-learn Diabetes	442	regression	Linear Regression	0.4493 R²	0.4526 R²	3.17 s

Candidate selection uses cross-validation on the training partition only. The test score is computed once after selection. Timings vary by hardware and installed optional dependencies. Run the same path with python scripts/benchmark.py once optional benchmark automation is added.