This is an experimental tool that gives model individual score explanation for an already trained scikit-learn model.

Model explanation provides the ability to interpret the effect of the predictors on the composition of an individual score. These predictors can then be ranked according to their contribution in the final score (leading to a positive or negative decision).

sklearn_explain is developed and tested using a python 3.5 version.

sklearn_explain is distributed under the 3-Clause BSD license.

This is a work in progress. The set of features is evolving. Your feature requests, issues, comments, help, hints are very welcome.

also availabe as a jupyter notebook

from sklearn import datasets
import pandas as pd
import sklearn_explain.explainer as expl
ds = datasets.load_breast_cancer();
from sklearn.ensemble import RandomForestClassifier
clf = RandomForestClassifier(n_estimators=120, random_state = 1960)
clf.fit(ds.data , ds.target)
lExplainer = expl.cModelScoreExplainer(clf)
lExplainer.mFeatureNames = ds.feature_names
lExplainer.fit(ds.data)
df_rc = lExplainer.explain(ds.data)
print(df_rc.head())

sklearn_explain has been developed, tested and used on a python 3.5 version.

The following commands install sklearn_explain and all its dependencies:

pip install scipy pandas sklearn # optional
pip install --upgrade git+git://github.com/antoinecarme/sklearn_explain.git