xgb.model.dt.tree {xgboost} | R Documentation

## Parse a boosted tree model text dump

### Description

Parse a boosted tree model text dump into a `data.table` structure.

### Usage

    xgb.model.dt.tree(feature_names = NULL, model = NULL, text = NULL,
      trees = NULL, use_int_id = FALSE, ...)

### Arguments

`feature_names` | 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.
`model` | object of class `xgb.Booster`
`text` | `character` vector previously generated by the `xgb.dump` function (where parameter `with_stats = TRUE` should have been set). `text` takes precedence over `model`.
`trees` | 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).
`use_int_id` | 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.

### Value

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.

### Examples

    # Basic use:

    data(agaricus.train, package='xgboost')

    bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label, max_depth = 2,
                   eta = 1, nthread = 2, 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)]

[Package xgboost version 0.90.0.2 Index]