sklearn_interface

AutoTabPFNClassifier

Bases: ClassifierMixin, BaseEstimator

Automatic Post Hoc Ensemble Classifier for TabPFN models.

Parameters

max_time : int | None, default=None
    The maximum time to spend on fitting the post hoc ensemble.
preset: {"default", "custom_hps", "avoid_overfitting"}, default="default"
    The preset to use for the post hoc ensemble.
ges_scoring_string : str, default="roc"
    The scoring string to use for the greedy ensemble search.
    Allowed values are: {"accuracy", "roc" / "auroc", "f1", "log_loss"}.
device : {"cpu", "cuda"}, default="cuda"
    The device to use for training and prediction.
random_state : int, RandomState instance or None, default=None
    Controls both the randomness base models and the post hoc ensembling method.
categorical_feature_indices: list[int] or None, default=None
    The indices of the categorical features in the input data. Can also be passed to `fit()`.
phe_init_args : dict | None, default=None
    The initialization arguments for the post hoc ensemble predictor.
    See post_hoc_ensembles.pfn_phe.AutoPostHocEnsemblePredictor for more options and all details.

predictor_ : AutoPostHocEnsemblePredictor
    The predictor interface used to make predictions, see post_hoc_ensembles.pfn_phe.AutoPostHocEnsemblePredictor for more.
phe_init_args_ : dict
    The optional initialization arguments used for the post hoc ensemble predictor.

AutoTabPFNRegressor

Bases: RegressorMixin, BaseEstimator

Automatic Post Hoc Ensemble Regressor for TabPFN models.

Parameters

max_time : int | None, default=None
    The maximum time to spend on fitting the post hoc ensemble.
preset: {"default", "custom_hps", "avoid_overfitting"}, default="default"
    The preset to use for the post hoc ensemble.
ges_scoring_string : str, default="mse"
    The scoring string to use for the greedy ensemble search.
    Allowed values are: {"rmse", "mse", "mae"}.
device : {"cpu", "cuda"}, default="cuda"
    The device to use for training and prediction.
random_state : int, RandomState instance or None, default=None
    Controls both the randomness base models and the post hoc ensembling method.
categorical_feature_indices: list[int] or None, default=None
    The indices of the categorical features in the input data. Can also be passed to `fit()`.
phe_init_args : dict | None, default=None
    The initialization arguments for the post hoc ensemble predictor.
    See post_hoc_ensembles.pfn_phe.AutoPostHocEnsemblePredictor for more options and all details.

predictor_ : AutoPostHocEnsemblePredictor
    The predictor interface used to make predictions, see post_hoc_ensembles.pfn_phe.AutoPostHocEnsemblePredictor for more.
phe_init_args_ : dict
    The optional initialization arguments used for the post hoc ensemble predictor.