Explain & understand

Open the black box: which features mattered, how they change the prediction, and a plain-English summary.

explain()

explain(model, df, target_col=None)Run here

One call for a full plain-English explanation of the model: top features, direction of effect, and caveats.

In plain English: A plain-English summary of what the model leans on most and which way each thing pushes the answer.

Try it yourself

Output

Press Run to execute this snippet.

explain.permutation_importance(model, df, target, n_repeats=5)

Shuffle each feature and see how much the score drops; a model-agnostic, trustworthy importance ranking.

In plain English: Ranks which columns actually matter by scrambling each one and seeing how much the score drops.

Exampleruns locally after pip install

import breezeml

breezeml.explain.permutation_importance(model, df, "target")

explain.partial_dependence(model, df, target, feature)

Shows how the prediction changes as one feature moves, holding the rest fixed. The 'what-if' curve.

In plain English: A what-if curve: slide one feature up and down and watch the prediction move, everything else held still.

Exampleruns locally after pip install

import breezeml

breezeml.explain.partial_dependence(model, df, "target", feature="age")

features.select(df, target, k=10) · features.pca(df) · features.polynomial(df)

Pick the k most informative columns, compress with PCA, or expand with polynomial interactions, all one-liners.

In plain English: Trim to the columns that matter, squeeze many columns into a few, or invent new combined ones, each in a line.

Exampleruns locally after pip install

import breezeml

top = breezeml.features.select(df, "target", k=10)
reduced = breezeml.features.pca(df, n_components=0.95)