lightgbm-org--lightgbm
wehub-resource-sync
64 行
1.8 KiB
R
64 行
1.8 KiB
R
% Generated by roxygen2: do not edit by hand
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% Please edit documentation in R/lgb.interpret.R
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\name{lgb.interpret}
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\alias{lgb.interpret}
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\title{Compute feature contribution of prediction}
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\usage{
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lgb.interpret(model, data, idxset, num_iteration = NULL)
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}
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\arguments{
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\item{model}{object of class \code{lgb.Booster}.}
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\item{data}{a matrix object or a dgCMatrix object.}
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\item{idxset}{an integer vector of indices of rows needed.}
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\item{num_iteration}{number of iteration want to predict with, NULL or <= 0 means use best iteration.}
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}
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\value{
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For regression, binary classification and lambdarank model, a \code{list} of \code{data.table}
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with the following columns:
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\itemize{
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\item{\code{Feature}: Feature names in the model.}
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\item{\code{Contribution}: The total contribution of this feature's splits.}
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}
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For multiclass classification, a \code{list} of \code{data.table} with the Feature column and
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Contribution columns to each class.
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}
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\description{
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Computes feature contribution components of rawscore prediction.
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}
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\examples{
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\donttest{
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\dontshow{setLGBMthreads(2L)}
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\dontshow{data.table::setDTthreads(1L)}
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Logit <- function(x) log(x / (1.0 - x))
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data(agaricus.train, package = "lightgbm")
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train <- agaricus.train
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dtrain <- lgb.Dataset(train$data, label = train$label)
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set_field(
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dataset = dtrain
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, field_name = "init_score"
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, data = rep(Logit(mean(train$label)), length(train$label))
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)
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data(agaricus.test, package = "lightgbm")
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test <- agaricus.test
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params <- list(
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objective = "binary"
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, learning_rate = 0.1
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, max_depth = -1L
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, min_data_in_leaf = 1L
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, min_sum_hessian_in_leaf = 1.0
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, num_threads = 2L
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)
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model <- lgb.train(
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params = params
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, data = dtrain
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, nrounds = 3L
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)
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tree_interpretation <- lgb.interpret(model, test$data, 1L:5L)
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}
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}
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