Computes feature contribution components of rawscore prediction.
lgb.interprete(model, data, idxset, num_iteration = NULL)object of class lgb.Booster.
a matrix object or a dgCMatrix object.
an integer vector of indices of rows needed.
number of iteration want to predict with, NULL or <= 0 means use best iteration.
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.
# \donttest{
setLGBMthreads(2L)
data.table::setDTthreads(1L)
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
, num_threads = 2L
)
model <- lgb.train(
params = params
, data = dtrain
, nrounds = 3L
)
#> [LightGBM] [Info] Number of positive: 3140, number of negative: 3373
#> [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000985 seconds.
#> You can set `force_row_wise=true` to remove the overhead.
#> And if memory is not enough, you can set `force_col_wise=true`.
#> [LightGBM] [Info] Total Bins 232
#> [LightGBM] [Info] Number of data points in the train set: 6513, number of used features: 116
#> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
#> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
#> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
tree_interpretation <- lgb.interprete(model, test$data, 1L:5L)
# }