hyperimpute.plugins.prediction.classifiers.plugin_catboost module

class CatBoostPlugin(n_estimators: Optional[int] = 10, depth: Optional[int] = None, grow_policy: int = 0, model: Optional[Any] = None, hyperparam_search_iterations: Optional[int] = None, random_state: int = 0, l2_leaf_reg: float = 3, learning_rate: float = 0.001, min_data_in_leaf: int = 1, random_strength: float = 1, **kwargs: Any)

Bases: ClassifierPlugin

Classification plugin based on the CatBoost framework.

Method:

CatBoost provides a gradient boosting framework which attempts to solve for Categorical features using a permutation driven alternative compared to the classical algorithm. It uses Ordered Boosting to overcome over fitting and Symmetric Trees for faster execution.

Parameters:

• learning_rate – float The learning rate used for training.

• depth – int

• iterations – int

• grow_policy – int

Example

>>> from hyperimpute.plugins.prediction import Predictions
>>> plugin = Predictions(category="classifiers").get("catboost")
>>> from sklearn.datasets import load_iris
>>> X, y = load_iris(return_X_y=True)
>>> plugin.fit_predict(X, y) # returns the probabilities for each class

_abc_impl = <_abc_data object>

_fit(X: DataFrame, *args: Any, **kwargs: Any) → CatBoostPlugin

_predict(X: DataFrame, *args: Any, **kwargs: Any) → DataFrame

_predict_proba(X: DataFrame, *args: Any, **kwargs: Any) → DataFrame

grow_policies: List[Optional[str]] = [None, 'Depthwise', 'SymmetricTree', 'Lossguide']

static hyperparameter_space(*args: Any, **kwargs: Any) → List[Params]

static name() → str

plugin

alias of CatBoostPlugin