Compute feature contribution of prediction

Computes feature contribution components of rawscore prediction.

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

Arguments

model	object of class lgb.Booster.
data	a matrix object or a dgCMatrix object.
idxset	a 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

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)

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