60 lines
3.1 KiB
R
60 lines
3.1 KiB
R
% Generated by roxygen2 (4.1.0): do not edit by hand
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% Please edit documentation in R/xgb.model.dt.tree.R
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\name{xgb.model.dt.tree}
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\alias{xgb.model.dt.tree}
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\title{Convert tree model dump to data.table}
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\usage{
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xgb.model.dt.tree(feature_names = NULL, filename_dump = NULL,
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model = NULL, text = NULL, n_first_tree = NULL)
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}
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\arguments{
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\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}.}
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\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}).}
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\item{model}{dump generated by the \code{xgb.train} function. Avoid the creation of a dump file.}
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\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}).}
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\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.}
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}
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\value{
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A \code{data.table} of the features used in the model with their gain, cover and few other thing.
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}
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\description{
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Read a tree model text dump and return a data.table.
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}
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\details{
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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.
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The content of the \code{data.table} is organised that way:
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\itemize{
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\item \code{ID}: unique identifier of a node ;
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\item \code{Feature}: feature used in the tree to operate a split. When Leaf is indicated, it is the end of a branch ;
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\item \code{Split}: value of the chosen feature where is operated the split ;
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\item \code{Yes}: ID of the feature for the next node in the branch when the split condition is met ;
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\item \code{No}: ID of the feature for the next node in the branch when the split condition is not met ;
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\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 ;
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\item \code{Quality}: it's the gain related to the split in this specific node ;
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\item \code{Cover}: metric to measure the number of observation affected by the split ;
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\item \code{Tree}: ID of the tree. It is included in the main ID ;
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\item \code{Yes.X} or \code{No.X}: data related to the pointer in \code{Yes} or \code{No} column ;
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}
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}
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\examples{
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data(agaricus.train, package='xgboost')
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#Both dataset are list with two items, a sparse matrix and labels
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#(labels = outcome column which will be learned).
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#Each column of the sparse Matrix is a feature in one hot encoding format.
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train <- agaricus.train
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bst <- xgboost(data = train$data, label = train$label, max.depth = 2,
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eta = 1, nthread = 2, nround = 2,objective = "binary:logistic")
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#agaricus.test$data@Dimnames[[2]] represents the column names of the sparse matrix.
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xgb.model.dt.tree(agaricus.train$data@Dimnames[[2]], model = bst)
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}
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