verticapy.machine_learning.memmodel.ensemble.XGBClassifier.predict_proba#

XGBClassifier.predict_proba(X: list | ndarray) → ndarray#

Computes the model’s probabilites using the input matrix.

Parameters#

X: list | numpy.array: The data on which to make the prediction.

Returns#

numpy.array: Probabilities.

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 XGBClassifier

model_xgbc = XGBClassifier(
    trees = [model1, model2, model3],
    classes = ["a", "b", "c"],
    logodds = [0.1, 0.12, 0.15],
    learning_rate = 0.1,
)

Create a dataset.

data = [["male", 100], ["female", 20], ["female", 50]]

Compute the predictions.

model_xgbc.predict_proba(data)
Out[8]: 
array([[0.34318847, 0.32840576, 0.32840576],
       [0.32393829, 0.34024456, 0.33581715],
       [0.32394919, 0.33138502, 0.34466579]])

Note

Refer to XGBClassifier for more information about the different methods and usages.