% 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 = 10, cols = 1,
  left_margin = 10, 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{

Sigmoid <- function(x) 1 / (1 + exp(-x))
Logit <- function(x) log(x / (1 - 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 = 63, max_depth = -1,
              min_data_in_leaf = 1, min_sum_hessian_in_leaf = 1)
              model <- lgb.train(params, dtrain, 20)
model <- lgb.train(params, dtrain, 20)

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

}