Computes feature contribution components of rawscore prediction.

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

model | object of class |
---|---|

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. |

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.

Sigmoid <- function(x) 1.0 / (1.0 + exp(-x)) Logit <- function(x) log(x / (1.0 - 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 = 63L , max_depth = -1L , min_data_in_leaf = 1L , min_sum_hessian_in_leaf = 1.0 ) model <- lgb.train(params, dtrain, 10L) tree_interpretation <- lgb.interprete(model, test$data, 1L:5L)