verticapy.machine_learning.memmodel.ensemble.XGBRegressor.predict#

XGBRegressor.predict(X: list | ndarray) → ndarray#

Predicts using the XGBRegressor model.

Parameters#

X: ArrayLike: The data on which to make the prediction.

Returns#

numpy.array: Predicted values.

Examples#

Import the required modules and create many BinaryTreeRegressor.

from verticapy.machine_learning.memmodel.tree import BinaryTreeRegressor

model1 = BinaryTreeRegressor(
    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, 3, 11, 23],
)


model2 = BinaryTreeRegressor(
    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, -3, 12, 56],
)


model3 = BinaryTreeRegressor(
    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, 1, 3, 6],
)

Let’s create a model.

from verticapy.machine_learning.memmodel.ensemble import XGBRegressor

model_xgbr = XGBRegressor(
    trees = [model1, model2, model3],
    mean = 2.5,
    eta = 0.9,
)

Create a dataset.

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

Compute the predictions.

model_xgbr.predict(data)
Out[8]: array([ 3.4, 25.9, 79. ])

Note

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