lgb.model.dt.tree.Rd

% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/lgb.model.dt.tree.R
\name{lgb.model.dt.tree}
\alias{lgb.model.dt.tree}
\title{Parse a LightGBM model json dump}
\usage{
lgb.model.dt.tree(model, num_iteration = NULL)
}
\arguments{
\item{model}{object of class \code{lgb.Booster}}

\item{num_iteration}{number of iterations you want to predict with. NULL or
<= 0 means use best iteration}
}
\value{
A \code{data.table} with detailed information about model trees' nodes and leafs.

The columns of the \code{data.table} are:

\itemize{
 \item \code{tree_index}: ID of a tree in a model (integer)
 \item \code{split_index}: ID of a node in a tree (integer)
 \item \code{split_feature}: for a node, it's a feature name (character);
                             for a leaf, it simply labels it as \code{"NA"}
 \item \code{node_parent}: ID of the parent node for current node (integer)
 \item \code{leaf_index}: ID of a leaf in a tree (integer)
 \item \code{leaf_parent}: ID of the parent node for current leaf (integer)
 \item \code{split_gain}: Split gain of a node
 \item \code{threshold}: Splitting threshold value of a node
 \item \code{decision_type}: Decision type of a node
 \item \code{default_left}: Determine how to handle NA value, TRUE -> Left, FALSE -> Right
 \item \code{internal_value}: Node value
 \item \code{internal_count}: The number of observation collected by a node
 \item \code{leaf_value}: Leaf value
 \item \code{leaf_count}: The number of observation collected by a leaf
}
}
\description{
Parse a LightGBM model json dump into a \code{data.table} structure.
}
\examples{

data(agaricus.train, package = "lightgbm")
train <- agaricus.train
dtrain <- lgb.Dataset(train$data, label = train$label)

params <- list(objective = "binary",
               learning_rate = 0.01, num_leaves = 63, max_depth = -1,
               min_data_in_leaf = 1, min_sum_hessian_in_leaf = 1)
model <- lgb.train(params, dtrain, 10)

tree_dt <- lgb.model.dt.tree(model)

}