BreezeML wraps scikit-learn so a beginner can go from a spreadsheet to an honest, deployable model in three lines, while an expert keeps full control. Here is what makes it special. Every promise below is a real feature.
fit(df, target) -> modelRun hereNo pipelines to wire, no config files. Give it a table and the name of the column you want to predict. BreezeML figures out the rest (classification vs regression, cleaning, encoding, scaling, cross-validation) and hands back a trained model.
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auto(df, target, explain_decisions=True)Set explain_decisions=True and BreezeML narrates every choice it makes in plain English as it makes it: why it picked a model, how it cleaned the data, which metric it optimized. It teaches while it works.
import breezeml
df = breezeml.datasets.wine()
model, report = breezeml.auto(
df, "wine_class", explain_decisions=True
)export(model, 'train.py')export() writes a standalone scikit-learn script that reproduces your exact pipeline with NO breezeml import. Delete the library and your model still runs. You are never trapped.
import breezeml
df = breezeml.datasets.iris()
model = breezeml.fit(df, "species")
breezeml.export(model, "train.py") # pure sklearnscikit-learn · pandas · numpy · joblibThe core installs with only four packages, and a CI test fails the build if anyone adds a fifth. That discipline is why the whole library can even run inside this web page.
# the entire core dependency list
# scikit-learn, pandas, numpy, joblib
# (xgboost, lightgbm, onnx, etc. are optional extras)audit() · conformal · fairness · drift · significanceRun hereMost tools shout a single accuracy number. BreezeML ships a whole honesty toolkit: leakage detection, fairness gaps, drift monitoring, statistical significance, and distribution-free uncertainty. See the Honesty section.
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breezeml-mcpA built-in Model Context Protocol server lets Claude and other agents train, compare, explain, and deploy models with sound statistical defaults, and get the same explanations you would.
pip install "breezeml[mcp]"
claude mcp add breezeml -- breezeml-mcpzen() · haiku() · fortune() · sensei()When the work is done, breezeml.zen() rakes the gravel: a breathing pause, a haiku about your model, a fortune, or a word from the sensei. Machine learning, with calm.
import breezeml
breezeml.haiku()
breezeml.fortune()