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.

  • 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, ...)



Wrapped model, result of [train].


Additional parameters, which are passed to the underlying importance value generating function.


([FeatureImportance]) An object containing a `data.frame` of the variable importances and further information.