AutoTabPFNClassifier ¶
Bases: BaseEstimator
, ClassifierMixin
Automatic Post Hoc Ensemble Classifier for TabPFN models.
Attributes:
Name | Type | Description |
---|---|---|
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. |
__init__ ¶
__init__(
max_time: int | None = 30,
preset: Literal[
"default", "custom_hps", "avoid_overfitting"
] = "default",
ges_scoring_string: str = "roc",
device: Literal["cpu", "cuda"] = "cpu",
random_state: int | None | RandomState = None,
categorical_feature_indices: list[int] | None = None,
phe_init_args: dict | None = None,
)
Parameters:
Name | Type | Description | Default |
---|---|---|---|
max_time |
int | None, default=None The maximum time to spend on fitting the post hoc ensemble. |
30
|
|
preset |
Literal['default', 'custom_hps', 'avoid_overfitting']
|
{"default", "custom_hps", "avoid_overfitting"}, default="default" The preset to use for the post hoc ensemble. |
'default'
|
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"}. |
'roc'
|
|
device |
{"cpu", "cuda"}, default="cuda" The device to use for training and prediction. |
'cpu'
|
|
random_state |
int, RandomState instance or None, default=None Controls both the randomness base models and the post hoc ensembling method. |
None
|
|
categorical_feature_indices |
list[int] | None
|
list[int] or None, default=None
The indices of the categorical features in the input data. Can also be passed to |
None
|
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. |
None
|