vastorbit.machine_learning.memmodel.ensemble.RandomForestClassifier.predict_proba_sql

RandomForestClassifier.predict_proba_sql(X: Annotated[list | ndarray, 'Array Like Structure']) → list[str]

Returns the SQL code needed to deploy the model using its attributes.

Parameters:

X (list | numpy.array) – The names or values of the input predictors.

Returns:

SQL code.

Return type:

str

Examples

Import the required modules and create many BinaryTreeClassifier.

from vastorbit.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 vastorbit.machine_learning.memmodel.ensemble import RandomForestClassifier

model_rfc = RandomForestClassifier(
    trees = [model1, model2, model3],
    classes = ["a", "b", "c"],
)

Let’s use the following column names:

cnames = ["sex", "fare"]

Get the SQL code needed to deploy the model.

model_rfc.predict_proba_sql(cnames)

Note

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