verticapy.machine_learning.memmodel.ensemble.RandomForestRegressor.predict#

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

Predicts using the Random Forest regressor 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, 3.0, 11.0, 23.5],
)


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 RandomForestRegressor

model_rfr = RandomForestRegressor(trees = [model1, model2, model3])

Create a dataset.

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

Compute the predictions.

model_rfr.predict(data)
Out[8]: array([ 0.33333333,  8.66666667, 28.5       ])

Note

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