% Generated by roxygen2 (4.1.0): do not edit by hand
% Please edit documentation in R/xgb.importance.R
\name{xgb.importance}
\alias{xgb.importance}
\title{Show importance of features in a model}
\usage{
xgb.importance(feature_names = NULL, filename_dump = NULL)
}
\arguments{
\item{feature_names}{names of each feature as a character vector. Can be extracted from a sparse matrix (see example). If model dump already contains feature names, this argument should be \code{NULL}.}

\item{filename_dump}{the path to the text file storing the model.}
}
\description{
Read a xgboost model in text file format.
Can be tree or linear model (text dump of linear model are only supported in dev version of Xgboost for now).
}
\details{
Return a data.table of the features with their weight.
#'
}
\examples{
data(agaricus.train, package='xgboost')
data(agaricus.test, package='xgboost')

#Both dataset are list with two items, a sparse matrix and labels (labels = outcome column which will be learned).
#Each column of the sparse Matrix is a feature in one hot encoding format.
train <- agaricus.train
test <- agaricus.test

bst <- xgboost(data = train$data, label = train$label, max.depth = 2,
               eta = 1, nround = 2,objective = "binary:logistic")
xgb.dump(bst, 'xgb.model.dump', with.stats = T)

#agaricus.test$data@Dimnames[[2]] represents the column names of the sparse matrix.
xgb.importance(agaricus.test$data@Dimnames[[2]], 'xgb.model.dump')
}