verticapy.machine_learning.memmodel.cluster.BisectingKMeans.plot_tree#

BisectingKMeans.plot_tree(pic_path: str | None = None, *args, **kwargs) → Source#

Draws the input tree. Requires the graphviz module.

Parameters#

pic_path: str, optional: Absolute path to save the image of the tree.
*args, **kwargs: Any, optional: Arguments to pass to the to_graphviz method.

Returns#

graphviz.Source: graphviz object.

Examples#

Import the required module.

from verticapy.machine_learning.memmodel.tree import BinaryTreeClassifier

We will use the following attributes:

# Different Attributes
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"]

Let’s create a model.

# Building the Model
model_btc = BinaryTreeClassifier(
    children_left = children_left,
    children_right = children_right,
    feature = feature,
    threshold = threshold,
    value = value,
    classes = classes,
)

Let’s draw the input tree.

model_btc.plot_tree()

../_images/machine_learning_memmodel_tree_binarytreeclassifier.png

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.