% Generated by roxygen2 (4.0.1): do not edit by hand
\name{xgb.model.dt.tree}
\alias{xgb.model.dt.tree}
\title{Convert tree model dump to data.table}
\usage{
xgb.model.dt.tree(feature_names = NULL, filename_dump = NULL,
  model = NULL, text = NULL, n_first_tree = 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. Model dump must include the gain per feature and per tree (parameter \code{with.stats = T} in function \code{xgb.dump}).}

\item{model}{dump generated by the \code{xgb.train} function. Avoid the creation of a dump file.}

\item{text}{dump generated by the \code{xgb.dump} function. Avoid the creation of a dump file. Model dump must include the gain per feature and per tree (parameter \code{with.stats = T} in function \code{xgb.dump}).}

\item{n_first_tree}{limit the plot to the n first trees. If \code{NULL}, all trees of the model are plotted. Performance can be low for huge models.}
}
\value{
A \code{data.table} of the features used in the model with their gain, cover and few other thing.
}
\description{
Read a tree model text dump and return a data.table.
}
\details{
General function to convert a text dump of tree model to a Matrix. The purpose is to help user to explore the model and get a better understanding of it.

The content of the \code{data.table} is organised that way:

\itemize{
\item \code{ID}: unique identifier of a node ;
 \item \code{Feature}: feature used in the tree to operate a split. When Leaf is indicated, it is the end of a branch ;
 \item \code{Split}: value of the chosen feature where is operated the split ;
 \item \code{Yes}: ID of the feature for the next node in the branch when the split condition is met ;
 \item \code{No}: ID of the feature for the next node in the branch when the split condition is not met ;
 \item \code{Missing}: ID of the feature for the next node in the branch for observation where the feature used for the split are not provided ;
 \item \code{Quality}: it's the gain related to the split in this specific node ;
 \item \code{Cover}: metric to measure the number of observation affected by the split ;
 \item \code{Tree}: ID of the tree. It is included in the main ID ;
}
}
\examples{
data(agaricus.train, 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

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 = TRUE)

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