estimator ¶

TabPFNClassifier ¶

Bases: BaseEstimator, ClassifierMixin, TabPFNModelSelection

predict ¶

predict(X)

Predict class labels for samples in X.

Parameters:

Name	Type	Description	Default
`X`		The input samples.	required

Returns:

Type	Description
	The predicted class labels.

predict_proba ¶

predict_proba(X)

Predict class probabilities for X.

Parameters:

Name	Type	Description	Default
`X`		The input samples.	required

Returns:

Type	Description
	The class probabilities of the input samples.

TabPFNModelSelection ¶

Base class for TabPFN model selection and path handling.

TabPFNRegressor ¶

Bases: BaseEstimator, RegressorMixin, TabPFNModelSelection

predict ¶

predict(
    X: ndarray,
    output_type: Literal[
        "mean",
        "median",
        "mode",
        "quantiles",
        "full",
        "main",
    ] = "mean",
    quantiles: Optional[list[float]] = None,
) -> Union[ndarray, list[ndarray], dict[str, ndarray]]

Predict regression target for X.

Parameters¶

X : array-like of shape (n_samples, n_features) The input samples. output_type : str, default="mean" The type of prediction to return: - "mean": Return mean prediction - "median": Return median prediction - "mode": Return mode prediction - "quantiles": Return predictions for specified quantiles - "full": Return full prediction details - "main": Return main prediction metrics quantiles : list[float] or None, default=None Quantiles to compute when output_type="quantiles". Default is [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]

Returns¶

array-like or dict The predicted values.

validate_data_size ¶

validate_data_size(
    X: ndarray, y: Union[ndarray, None] = None
)

Check the integrity of the training data. - check if the number of rows between X and y is consistent if y is not None (ValueError) - check if the number of rows is less than MAX_ROWS (ValueError) - check if the number of columns is less than MAX_COLS (ValueError)