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Two-stage regression for non-negative targets with a point mass at zero.
pip install tabpfn-extensions
AutoHurdleRegressorNone when the hurdle is inactive. Both estimators are None
for an all-zero target with the hurdle enabled.
AutoHurdleRegressor(
classifier: Any = None,
regressor: Any = None,
*,
hurdle: Literal["auto"] | bool = "auto",
zero_threshold: float = 0.25,
quantile_grid: list[float] | tuple[float, ...] | np.ndarray | None = None,
)
| Parameter | Type | Default | Description |
|---|---|---|---|
classifier | Any | None | Cloneable classifier with predict_proba. Defaults to TabPFN. |
regressor | Any | None | Cloneable regressor with TabPFN’s output_type and quantiles prediction arguments. Defaults to TabPFN. |
hurdle | Literal[“auto”] | bool | "auto" | Whether to split zero and positive targets. “auto” enables the split for non-negative targets whose zero rate exceeds zero_threshold. Otherwise, all rows go to the regressor. |
zero_ | float | 0.25 | Training zero-rate threshold for automatic selection. Defaults to 0.25, a heuristic rather than a tuned optimum. This controls whether to fit two stages, independently of the prediction statistic. |
quantile_ | list[float] | tuple[float, …] | np.ndarray | None | None | Strictly increasing positive-stage quantile levels between 0 and 1, with at least two entries. None uses the 21-point grid from 0.01 to 0.99. Required levels outside the grid clamp to its endpoints. |
AutoHurdleRegressor.fitAutoHurdleRegressor.fit(
X: Any,
y: Any,
) -> AutoHurdleRegressor
| Parameter | Type | Default | Description |
|---|---|---|---|
X | Any | Required | — |
y | Any | Required | — |
| Type | Description |
|---|---|
Auto | — |
AutoHurdleRegressor.predictAutoHurdleRegressor.predict(
X: Any,
*,
output_type: Literal["mean", "median", "quantiles"] = "median",
quantiles: list[float] | None = None,
) -> np.ndarray | list[np.ndarray]
| Parameter | Type | Default | Description |
|---|---|---|---|
X | Any | Required | — |
output_ | Literal[“mean”, “median”, “quantiles”] | "median" | — |
quantiles | list[float] | None | None | — |
| Type | Description |
|---|---|
np.ndarray | list[np.ndarray] | — |
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