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.
Measure which accounts the gain of Gini index given by a feature in a tree and the weight of that tree.
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.
Estimation of relative influence for each feature. See relative.influence for details and further parameters.
Relative feature importances as returned by varimp.
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.
This is identical to randomForest.
This method can calculate feature importance for various measures. By default the Breiman-Cutler permutation method is used. See vimp for details.
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.
Sum of decrease in impurity for each of the surrogate variables at each node.
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.
([FeatureImportance]) An object containing a `data.frame` of the variable importances and further information.