Philosophy

The core installs with only four packages, and a CI test fails the build if anyone adds a fifth. Small footprint means fast installs, fewer version conflicts, and code you can actually read. It is also why the whole library can run inside this web page. Heavy extras like xgboost and the MCP server stay strictly opt-in.

export() writes a plain scikit-learn script that reproduces your exact pipeline with no breezeml import anywhere. Delete the library and your model still trains and runs. You adopt BreezeML for the workflow, never because you are trapped.

Most tools shout a single accuracy number. BreezeML assumes a score is a claim that has to survive leakage checks, a naive baseline, class balance, and fairness before you trust it. report() folds all of that into one verdict, so the honest path is also the easy path.

A newcomer should go from a spreadsheet to an honest model in three lines, learning the ideas along the way, while an expert keeps every knob. Plain-English explanations, sensible defaults, and a calm tone are features, not decoration. Good tools teach while they work.