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Parse a boosted tree model text dump into a data.table
structure.
xgb.model.dt.tree(
feature_names = NULL,
model = NULL,
text = NULL,
trees = NULL,
use_int_id = FALSE,
...
)
A data.table
with detailed information about model trees' nodes.
The columns of the data.table
are:
Tree
: integer ID of a tree in a model (zero-based index)
Node
: integer ID of a node in a tree (zero-based index)
ID
: character identifier of a node in a model (only when use_int_id=FALSE
)
Feature
: for a branch node, it's a feature id or name (when available);
for a leaf note, it simply labels it as 'Leaf'
Split
: location of the split for a branch node (split condition is always "less than")
Yes
: ID of the next node when the split condition is met
No
: ID of the next node when the split condition is not met
Missing
: ID of the next node when branch value is missing
Quality
: either the split gain (change in loss) or the leaf value
Cover
: metric related to the number of observation either seen by a split
or collected by a leaf during training.
When use_int_id=FALSE
, columns "Yes", "No", and "Missing" point to model-wide node identifiers
in the "ID" column. When use_int_id=TRUE
, those columns point to node identifiers from
the corresponding trees in the "Node" column.
character vector of feature names. If the model already
contains feature names, those would be used when feature_names=NULL
(default value).
Non-null feature_names
could be provided to override those in the model.
object of class xgb.Booster
character
vector previously generated by the xgb.dump
function (where parameter with_stats = TRUE
should have been set).
text
takes precedence over model
.
an integer vector of tree indices that should be parsed.
If set to NULL
, all trees of the model are parsed.
It could be useful, e.g., in multiclass classification to get only
the trees of one certain class. IMPORTANT: the tree index in xgboost models
is zero-based (e.g., use trees = 0:4
for first 5 trees).
a logical flag indicating whether nodes in columns "Yes", "No", "Missing" should be represented as integers (when FALSE) or as "Tree-Node" character strings (when FALSE).
currently not used.
# Basic use:
data(agaricus.train, package='xgboost')
## Keep the number of threads to 1 for examples
nthread <- 1
data.table::setDTthreads(nthread)
bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label, max_depth = 2,
eta = 1, nthread = nthread, nrounds = 2,objective = "binary:logistic")
(dt <- xgb.model.dt.tree(colnames(agaricus.train$data), bst))
# This bst model already has feature_names stored with it, so those would be used when
# feature_names is not set:
(dt <- xgb.model.dt.tree(model = bst))
# How to match feature names of splits that are following a current 'Yes' branch:
merge(dt, dt[, .(ID, Y.Feature=Feature)], by.x='Yes', by.y='ID', all.x=TRUE)[order(Tree,Node)]
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