Posted by
that shows how to use .
The dtreeviz library, first released in 2018, is now the most popular visualization library for decision trees. The library is constantly being updated and improved, and there is a large community of users who can and article on the .
To interpret decision tree predictions we use dtreeviz to visualize how each decision node in the tree splits up a specific feature's domain, and to show the distribution of training instances in each leaf. For example, here is the first few levels of a classification tree from a Random Forest trained on the for a description of the visualization elements.)
The code used to generate that tree is short. Given a classifier model called cmodel, we collect and wrap up all of the information about the data and model then ask dtreeviz to visualize the tree:
penguin_features = [f.name for f in cmodel.make_inspector().features()]
penguin_label = "species" # Name of the classification target label
viz_cmodel = dtreeviz.model(cmodel,
tree_index=3, # pick tree from forest
X_train=train_ds_pd[penguin_features],
y_train=train_ds_pd[penguin_label],
feature_names=penguin_features,
target_name=penguin_label,
class_names=classes)
viz_cmodel.view()And here are the first few layers of a regressor tree from a Random Forest trained on the ! Then, try dtreeviz on your own tree models. To dig deeper into how decision trees are built and how they carve up feature space to make predictions, you can watch the . Enjoy!
SOCIAL SHARE CARD GENERATOR