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
,
lightgbm.LGBMRegressor
and lightgbm.Booster
estimators.
eli5.explain_weights()
uses feature importances. Additional
arguments for LGBMClassifier, LGBMClassifier and lightgbm.Booster:
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.
is_regression
- True if solving a regression problem and False for a classification problem. Needs to be passed only if it can’t be determined from other arguments.
target_names
argument is ignored for
lightgbm.LGBMClassifer
/ lightgbm.LGBMRegressor
,
but used for lightgbm.Booster
.
targets
argument is ignored.
Note
Top-level eli5.explain_weights()
calls are dispatched
to eli5.lightgbm.explain_weights_lightgbm()
for
lightgbm.LGBMClassifer
, lightgbm.LGBMRegressor
and lightgbm.Booster
.
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
, lightgbm.LGBMRegressor
and lightgbm.Booster
:
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.is_regression
- True if solving a regression problem and False for a classification problem. Needs to be passed only if it can’t be determined from other arguments.
Note
Top-level eli5.explain_prediction()
calls are dispatched
to eli5.xgboost.explain_prediction_lightgbm()
for
lightgbm.LGBMClassifer
, lightgbm.LGBMRegressor
and lightgbm.Booster
.