gpb.interprete: Compute feature contribution of prediction

In gpboost: Combining Tree-Boosting with Gaussian Process and Mixed Effects Models

Compute feature contribution of prediction

Description

Computes feature contribution components of rawscore prediction.

Usage

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

Arguments

model	object of class gpb.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 = "gpboost")
train <- agaricus.train
dtrain <- gpb.Dataset(train$data, label = train$label)
setinfo(dtrain, "init_score", rep(Logit(mean(train$label)), length(train$label)))
data(agaricus.test, package = "gpboost")
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 <- gpb.train(
    params = params
    , data = dtrain
    , nrounds = 3L
)

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

gpboost documentation built on Oct. 24, 2023, 9:09 a.m.