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verticapy.machine_learning.memmodel.ensemble.RandomForestRegressor.predict_sql#

RandomForestRegressor.predict_sql(X: list | ndarray) → str#

Returns the SQL code needed to deploy the model.

Parameters#

X: ArrayLike

The names or values of the input predictors.

Returns#

str

SQL code.

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])

Let’s use the following column names:

cnames = ["sex", "fare"]

Get the SQL code needed to deploy the model.

model_rfr.predict_sql(cnames)
Out[8]: "((CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 11.0 ELSE 23.5 END) ELSE 3.0 END) + (CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 12 ELSE 56 END) ELSE -3 END) + (CASE WHEN sex = 'female' THEN (CASE WHEN fare < 30 THEN 3 ELSE 6 END) ELSE 1 END)) / 3"

Note

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