lgb.plot.interpretation.Rd

% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/lgb.plot.interpretation.R
\name{lgb.plot.interpretation}
\alias{lgb.plot.interpretation}
\title{Plot feature contribution as a bar graph}
\usage{
lgb.plot.interpretation(
  tree_interpretation_dt,
  top_n = 10L,
  cols = 1L,
  left_margin = 10L,
  cex = NULL
)
}
\arguments{
\item{tree_interpretation_dt}{a \code{data.table} returned by \code{\link{lgb.interprete}}.}

\item{top_n}{maximal number of top features to include into the plot.}

\item{cols}{the column numbers of layout, will be used only for multiclass classification feature contribution.}

\item{left_margin}{(base R barplot) allows to adjust the left margin size to fit feature names.}

\item{cex}{(base R barplot) passed as \code{cex.names} parameter to \code{barplot}.}
}
\value{
The \code{lgb.plot.interpretation} function creates a \code{barplot}.
}
\description{
Plot previously calculated feature contribution as a bar graph.
}
\details{
The graph represents each feature as a horizontal bar of length proportional to the defined
contribution of a feature. Features are shown ranked in a decreasing contribution order.
}
\examples{
library(lightgbm)
Sigmoid <- function(x) {1.0 / (1.0 + exp(-x))}
Logit <- function(x) {log(x / (1.0 - x))}
data(agaricus.train, package = "lightgbm")
train <- agaricus.train
dtrain <- lgb.Dataset(train$data, label = train$label)
setinfo(dtrain, "init_score", rep(Logit(mean(train$label)), length(train$label)))
data(agaricus.test, package = "lightgbm")
test <- agaricus.test

params <- list(
  objective = "binary"
  , learning_rate = 0.01
  , num_leaves = 63L
  , max_depth = -1L
  , min_data_in_leaf = 1L
  , min_sum_hessian_in_leaf = 1.0
)
model <- lgb.train(params, dtrain, 10L)

tree_interpretation <- lgb.interprete(model, test$data, 1L:5L)
lgb.plot.interpretation(tree_interpretation[[1L]], top_n = 10L)
}