xgb.model.dt.tree
From xgboost v0.4-2
by Tong He
Convert tree model dump to data.table
Read a tree model text dump and return a data.table.
Usage
xgb.model.dt.tree(feature_names = NULL, filename_dump = NULL,
model = NULL, text = NULL, n_first_tree = NULL)
Arguments
- feature_names
- names of each feature as a character vector. Can be extracted from a sparse matrix (see example). If model dump already contains feature names, this argument should be
NULL
. - filename_dump
- the path to the text file storing the model. Model dump must include the gain per feature and per tree (parameter
with.stats = T
in functionxgb.dump
). - model
- dump generated by the
xgb.train
function. Avoid the creation of a dump file. - text
- dump generated by the
xgb.dump
function. Avoid the creation of a dump file. Model dump must include the gain per feature and per tree (parameterwith.stats = T
in functionxgb.dump
). - n_first_tree
- limit the plot to the n first trees. If
NULL
, all trees of the model are plotted. Performance can be low for huge models.
Details
General function to convert a text dump of tree model to a Matrix. The purpose is to help user to explore the model and get a better understanding of it.
The content of the data.table
is organised that way:
ID
: unique identifier of a node ;Feature
: feature used in the tree to operate a split. When Leaf is indicated, it is the end of a branch ;Split
: value of the chosen feature where is operated the split ;Yes
: ID of the feature for the next node in the branch when the split condition is met ;No
: ID of the feature for the next node in the branch when the split condition is not met ;Missing
: ID of the feature for the next node in the branch for observation where the feature used for the split are not provided ;Quality
: it's the gain related to the split in this specific node ;Cover
: metric to measure the number of observation affected by the split ;Tree
: ID of the tree. It is included in the main ID ;Yes.X
orNo.X
: data related to the pointer inYes
orNo
column ;
Value
- A
data.table
of the features used in the model with their gain, cover and few other thing.
Examples
data(agaricus.train, package='xgboost')
#Both dataset are list with two items, a sparse matrix and labels
#(labels = outcome column which will be learned).
#Each column of the sparse Matrix is a feature in one hot encoding format.
train <- agaricus.train
bst <- xgboost(data = train$data, label = train$label, max.depth = 2,
eta = 1, nthread = 2, nround = 2,objective = "binary:logistic")
#agaricus.test$data@Dimnames[[2]] represents the column names of the sparse matrix.
xgb.model.dt.tree(agaricus.train$data@Dimnames[[2]], model = bst)
Community examples
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