| getFeatureImportance {mlr} | R Documentation |
For some learners it is possible to calculate a feature importance measure. 'getFeatureImportance' extracts those values from trained models. See below for a list of supported learners.
boosting
Measure which accounts the gain of Gini index given by a feature
in a tree and the weight of that tree.
cforest
Permutation principle of the 'mean decrease in accuracy' principle
in randomForest. If 'auc=TRUE' (only for binary classification),
area under the curve is used as measure. The algorithm used for the survival
learner is 'extremely slow and experimental; use at your own risk'.
See varimp for details and further parameters.
gbm
Estimation of relative influence for each feature. See
relative.influence
for details and further parameters.
h2o
Relative feature importances as returned by varimp.
randomForest
For ‘type = 2' (the default) the ’MeanDecreaseGini' is measured,
which is based on the Gini impurity index used for the calculation of the nodes.
Alternatively, you can set 'type' to 1, then the measure is the mean
decrease in accuracy calculated on OOB data. Note, that in this case
the learner's parameter 'importance' needs to be set to be able to compute
feature importance values.
See importance for details.
RRF
This is identical to randomForest.
randomForestSRC
This method can calculate feature importance for
various measures. By default the Breiman-Cutler permutation method is used.
See vimp for details.
ranger
Supports both measures mentioned above for the randomForest
learner. Note, that you need to specifically set the learners parameter
'importance', to be able to compute feature importance measures.
See importance and
ranger for details.
rpart
Sum of decrease in impurity for each of the surrogate variables at each node.
xgboost
The value implies the relative contribution of the corresponding feature to the model
calculated by taking each feature's contribution for each tree in the model. The exact
computation of the importance in xgboost is undocumented.
getFeatureImportance(object, ...)
object |
([WrappedModel]) |
... |
(any) |
([FeatureImportance]) An object containing a 'data.frame' of the variable importances and further information.