lgb.interprete: Compute feature contribution of prediction

In lightgbm: Light Gradient Boosting Machine

Compute feature contribution of prediction

Description

Computes feature contribution components of rawscore prediction.

Usage

lgb.interprete(model, data, idxset, num_iteration = NULL)

Arguments

model	object of class lgb.Booster.
data	a matrix object or a dgCMatrix object.
idxset	an integer vector of indices of rows needed.
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 list of data.table with the following columns:

• Feature: Feature names in the model.

• Contribution: The total contribution of this feature's splits.

For multiclass classification, a list of data.table with the Feature column and Contribution columns to each class.

Examples

Logit <- function(x) log(x / (1.0 - x))
data(agaricus.train, package = "lightgbm")
train <- agaricus.train
dtrain <- lgb.Dataset(train$data, label = train$label)
set_field(
  dataset = dtrain
  , field_name = "init_score"
  , data = rep(Logit(mean(train$label)), length(train$label))
)
data(agaricus.test, package = "lightgbm")
test <- agaricus.test

params <- list(
    objective = "binary"
    , learning_rate = 0.1
    , max_depth = -1L
    , min_data_in_leaf = 1L
    , min_sum_hessian_in_leaf = 1.0
)
model <- lgb.train(
    params = params
    , data = dtrain
    , nrounds = 3L
)

tree_interpretation <- lgb.interprete(model, test$data, 1L:5L)

lightgbm documentation built on Jan. 17, 2023, 1:13 a.m.