# xgb.plot.multi.trees

0th

Percentile

##### Project all trees on one tree and plot it

Visualization of the ensemble of trees as a single collective unit.

##### Usage
xgb.plot.multi.trees(model, feature_names = NULL, features_keep = 5,
plot_width = NULL, plot_height = NULL, ...)
##### Arguments
model

dump generated by the xgb.train function.

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.

features_keep

number of features to keep in each position of the multi trees.

plot_width

width in pixels of the graph to produce

plot_height

height in pixels of the graph to produce

...

currently not used

##### Details

This function tries to capture the complexity of gradient boosted tree ensemble in a cohesive way.

The goal is to improve the interpretability of the model generally seen as black box. The function is dedicated to boosting applied to decision trees only.

The purpose is to move from an ensemble of trees to a single tree only.

It takes advantage of the fact that the shape of a binary tree is only defined by its deepness (therefore in a boosting model, all trees have the same shape).

Moreover, the trees tend to reuse the same features.

The function will project each tree on one, and keep for each position the features_keep first features (based on Gain per feature measure).

This function is inspired by this blog post: https://wellecks.wordpress.com/2015/02/21/peering-into-the-black-box-visualizing-lambdamart/

##### Value

Two graphs showing the distribution of the model deepness.

##### Aliases
• xgb.plot.multi.trees
##### Examples
# NOT RUN {
data(agaricus.train, package='xgboost')

bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label, max_depth = 15,
eta = 1, nthread = 2, nrounds = 30, objective = "binary:logistic",
min_child_weight = 50)

p <- xgb.plot.multi.trees(model = bst, feature_names = colnames(agaricus.train\$data),
features_keep = 3)
print(p)

# }