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lgb.interprete.Rd
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lgb.interprete.Rd
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% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/lgb.interprete.R
\name{lgb.interprete}
\alias{lgb.interprete}
\title{Compute feature contribution of prediction}
\usage{
lgb.interprete(model, data, idxset, num_iteration = NULL)
}
\arguments{
\item{model}{object of class \code{lgb.Booster}.}
\item{data}{a matrix object or a dgCMatrix object.}
\item{idxset}{a integer vector of indices of rows needed.}
\item{num_iteration}{number of iteration want to predict with, NULL or <= 0 means use best iteration.}
}
\value{
For regression, binary classification and lambdarank model, a \code{list} of \code{data.table} with the following columns:
\itemize{
\item \code{Feature} Feature names in the model.
\item \code{Contribution} The total contribution of this feature's splits.
}
For multiclass classification, a \code{list} of \code{data.table} with the Feature column and Contribution columns to each class.
}
\description{
Computes feature contribution components of rawscore prediction.
}
\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, 10)
tree_interpretation <- lgb.interprete(model, test$data, 1:5)
}