LightGBM¶
LightGBM is a fast Gradient Boosting framework; it provides a Python
interface. eli5 supports eli5.explain_weights()
and eli5.explain_prediction()
for lightgbm.LGBMClassifer
and lightgbm.LGBMRegressor
estimators.
eli5.explain_weights()
uses feature importances. Additional
arguments for LGBMClassifier and LGBMClassifier:
importance_type
is a way to get feature importance. Possible values are:- ‘gain’ - the average gain of the feature when it is used in trees (default)
- ‘split’ - the number of times a feature is used to split the data across all trees
- ‘weight’ - the same as ‘split’, for better compatibility with XGBoost.
target_names
and target
arguments are ignored.
Note
Top-level eli5.explain_weights()
calls are dispatched
to eli5.lightgbm.explain_weights_lightgbm()
for
lightgbm.LGBMClassifer
and lightgbm.LGBMRegressor
.
For eli5.explain_prediction()
eli5 uses an approach based on ideas from
http://blog.datadive.net/interpreting-random-forests/ :
feature weights are calculated by following decision paths in trees
of an ensemble. Each node of the tree has an output score, and
contribution of a feature on the decision path is how much the score changes
from parent to child.
Additional eli5.explain_prediction()
keyword arguments supported
for lightgbm.LGBMClassifer
and lightgbm.LGBMRegressor
:
vec
is a vectorizer instance used to transform raw features to the input of the estimatorlgb
(e.g. a fitted CountVectorizer instance); you can pass it instead offeature_names
.vectorized
is a flag which tells eli5 ifdoc
should be passed throughvec
or not. By default it is False, meaning that ifvec
is not None,vec.transform([doc])
is passed to the estimator. Set it to True if you’re passingvec
, butdoc
is already vectorized.
Note
Top-level eli5.explain_prediction()
calls are dispatched
to eli5.xgboost.explain_prediction_lightgbm()
for
lightgbm.LGBMClassifer
and lightgbm.LGBMRegressor
.