verticapy.machine_learning.memmodel.ensemble.Ensemble.plot_tree#
- Ensemble.plot_tree(pic_path: str | None = None, tree_id: int = 0, *args, **kwargs) Source #
Draws the input tree. Requires the Graphviz module.
Parameters#
Returns#
- graphviz.Source
Graphviz object.
Examples#
Import the required modules and create many
BinaryTreeClassifier
.from verticapy.machine_learning.memmodel.tree import BinaryTreeClassifier model1 = BinaryTreeClassifier( children_left = [1, 3, None, None, None], children_right = [2, 4, None, None, None], feature = [0, 1, None, None, None], threshold = ["female", 30, None, None, None], value = [None, None, [0.8, 0.1, 0.1], [0.1, 0.8, 0.1], [0.2, 0.2, 0.6]], classes = ["a", "b", "c"], ) model2 = BinaryTreeClassifier( children_left = [1, 3, None, None, None], children_right = [2, 4, None, None, None], feature = [0, 1, None, None, None], threshold = ["female", 30, None, None, None], value = [None, None, [0.7, 0.2, 0.1], [0.3, 0.5, 0.2], [0.2, 0.2, 0.6]], classes = ["a", "b", "c"], ) model3 = BinaryTreeClassifier( children_left = [1, 3, None, None, None], children_right = [2, 4, None, None, None], feature = [0, 1, None, None, None], threshold = ["female", 30, None, None, None], value = [None, None, [0.4, 0.4, 0.2], [0.2, 0.2, 0.6], [0.2, 0.5, 0.3]], classes = ["a", "b", "c"], )
Let’s create a model.
from verticapy.machine_learning.memmodel.ensemble import RandomForestClassifier model_rfc = RandomForestClassifier( trees = [model1, model2, model3], classes = ["a", "b", "c"], )
Let’s draw the input tree.
model_rfc.plot_tree(tree_id = 0)
Important
For this example, a specific model is utilized, and it may not correspond exactly to the model you are working with. To see a comprehensive example specific to your class of interest, please refer to that particular class.