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API: modeling.regression

skyulf.modeling.regression

Regression models.

RandomForestRegressorApplier

Bases: SklearnApplier

Random Forest Regressor Applier.

Source code in skyulf-core\skyulf\modeling\regression.py 
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class RandomForestRegressorApplier(SklearnApplier):
    """Random Forest Regressor Applier."""

    pass

RandomForestRegressorCalculator

Bases: SklearnCalculator

Random Forest Regressor Calculator.

Source code in skyulf-core\skyulf\modeling\regression.py 
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@NodeRegistry.register("random_forest_regressor", RandomForestRegressorApplier)
class RandomForestRegressorCalculator(SklearnCalculator):
    """Random Forest Regressor Calculator."""

    def __init__(self):
        super().__init__(
            model_class=RandomForestRegressor,
            default_params={
                "n_estimators": 50,
                "max_depth": 10,
                "min_samples_split": 5,
                "min_samples_leaf": 2,
                "n_jobs": -1,
                "random_state": 42,
            },
            problem_type="regression",
        )

RidgeRegressionApplier

Bases: SklearnApplier

Ridge Regression Applier.

Source code in skyulf-core\skyulf\modeling\regression.py 
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class RidgeRegressionApplier(SklearnApplier):
    """Ridge Regression Applier."""

    pass

RidgeRegressionCalculator

Bases: SklearnCalculator

Ridge Regression Calculator.

Source code in skyulf-core\skyulf\modeling\regression.py 
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@NodeRegistry.register("ridge_regression", RidgeRegressionApplier)
class RidgeRegressionCalculator(SklearnCalculator):
    """Ridge Regression Calculator."""

    def __init__(self):
        super().__init__(
            model_class=Ridge,
            default_params={
                "alpha": 1.0,
                "solver": "auto",
                "random_state": 42,
            },
            problem_type="regression",
        )