% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/getFeatureImportance.R
\name{getFeatureImportance}
\alias{getFeatureImportance}
\title{Calculates feature importance values for trained models.}
\usage{
getFeatureImportance(object, ...)
}
\arguments{
\item{object}{[\code{\link{WrappedModel}}]\cr
Wrapped model, result of \code{\link{train}}.}

\item{...}{[any]\cr
Additional parameters, which are passed to the underlying importance value 
generating function.}
}
\value{
[\code{FeatureImportance}] An object containing a \code{data.frame} of the variable importances and further information.
}
\description{
For some learners it is possible to calculate a feature importance measure.
\code{getFeatureImportance} extracts those values from trained models.
See below for a list of supported learners.


\itemize{
   \item{boosting} \cr
   {Measure which accounts the gain of Gini index given by a feature 
   in a tree and the weight of that tree.}
   \item{cforest} \cr
   {Permutation principle of the 'mean decrease in accuracy' principle
   in randomForest. If \code{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 \link[party:varimp]{varimp} for details and further parameters.}  
   \item{gbm} \cr
   {Estimation of relative influence for each feature. See 
   \link[gbm:relative.influence]{relative.influence}
   for details and further parameters.}
   \item{randomForest} \cr
   {For \code{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 \code{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 \code{importance} needs to be set to be able to compute
   feature importance values.
   See \link[randomForest:importance]{importance} for details.}
   \item{RRF} \cr
   {This is identical to randomForest.}
   \item{randomForestSRC} \cr
   {This method can calculate feature importance for
   various measures. By default the Breiman-Cutler permutation method is used.
   See \link[randomForestSRC:vimp]{vimp} for details.}
   \item{ranger} \cr
   {Supports both measures mentioned above for the randomForest
   learner. Note, that you need to specifically set the learners parameter
   \code{importance}, to be able to compute feature importance measures.
   See \link[ranger:importance]{importance} and 
   \link[ranger:ranger]{ranger} for details.}
   \item{rpart} \cr
   {Sum of decrease in impurity for each of the surrogate variables at each node.}
   \item{xgboost} \cr
   {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.}
 }
}
