Parse a LightGBM model json dump into a data.table structure.
lgb.model.dt.tree(model, num_iteration = NULL)
| model | object of class |
|---|---|
| num_iteration | number of iterations you want to predict with. NULL or <= 0 means use best iteration |
A data.table with detailed information about model trees' nodes and leafs.
The columns of the data.table are:
tree_index: ID of a tree in a model (integer)
split_index: ID of a node in a tree (integer)
split_feature: for a node, it's a feature name (character);
for a leaf, it simply labels it as "NA"
node_parent: ID of the parent node for current node (integer)
leaf_index: ID of a leaf in a tree (integer)
leaf_parent: ID of the parent node for current leaf (integer)
split_gain: Split gain of a node
threshold: Splitting threshold value of a node
decision_type: Decision type of a node
default_left: Determine how to handle NA value, TRUE -> Left, FALSE -> Right
internal_value: Node value
internal_count: The number of observation collected by a node
leaf_value: Leaf value
leaf_count: The number of observation collected by a leaf
# \donttest{ data(agaricus.train, package = "lightgbm") train <- agaricus.train dtrain <- lgb.Dataset(train$data, label = train$label) 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) #> [LightGBM] [Info] Number of positive: 3140, number of negative: 3373 #> [LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000997 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] [Info] [binary:BoostFromScore]: pavg=0.482113 -> initscore=-0.071580 #> [LightGBM] [Info] Start training from score -0.071580 #> [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 #> [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 #> [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 #> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf tree_dt <- lgb.model.dt.tree(model) # }