XGBoost¶
XGBoost is a popular Gradient Boosting library with Python interface.
eli5 supports eli5.explain_weights()
and eli5.explain_prediction()
for XGBClassifer, XGBRegressor and Booster estimators. It is tested for
xgboost >= 0.6a2.
eli5.explain_weights()
uses feature importances. Additional
arguments for XGBClassifer, XGBRegressor and 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)
- ‘weight’ - the number of times a feature is used to split the data across all trees
- ‘cover’ - the average coverage of the feature when it is used in trees
target_names
and targets
arguments are ignored.
Note
Top-level eli5.explain_weights()
calls are dispatched
to eli5.xgboost.explain_weights_xgboost()
for
XGBClassifer, XGBRegressor and 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.
Note
When explaining Booster predictions,
do not pass an xgboost.DMatrix
object as doc
, pass a numpy array
or a sparse matrix instead (or have vec
return them).
Additional eli5.explain_prediction()
keyword arguments supported
for XGBClassifer, XGBRegressor and Booster:
vec
is a vectorizer instance used to transform raw features to the input of the estimatorxgb
(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.
eli5.explain_prediction()
for Booster estimator accepts
two more optional arguments:
is_regression
- True if solving a regression problem (“objective” starts with “reg”) and False for a classification problem. If not set, regression is assumed for a single target estimator and proba will not be shown.missing
- set it to the same value as themissing
argument toxgboost.DMatrix
. Matters only if sparse values are used. Default isnp.nan
.
See the tutorial for a more detailed usage example.
Note
Top-level eli5.explain_prediction()
calls are dispatched
to eli5.xgboost.explain_prediction_xgboost()
for
XGBClassifer, XGBRegressor and Booster.