Documentation Index
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Fit the model.
TabPFNRegressor.fitTabPFNRegressor.fit(
X: XType,
y: YType,
) -> Self
Type aliases
XType = Any
YType = Any
| Type | Description |
|---|---|
Self | self |
TabPFNRegressor.predictTabPFNRegressor.predict(
X: XType,
*,
output_type: OutputType = "mean",
quantiles: list[float] | None = None,
) -> RegressionResultType
Overloaded signatures
TabPFNRegressor.predict(
X: XType,
*,
output_type: Literal["mean", "median", "mode"] = "mean",
quantiles: list[float] | None = None,
) -> np.ndarray
TabPFNRegressor.predict(
X: XType,
*,
output_type: Literal["quantiles"],
quantiles: list[float] | None = None,
) -> list[np.ndarray]
TabPFNRegressor.predict(
X: XType,
*,
output_type: Literal["main"],
quantiles: list[float] | None = None,
) -> MainOutputDict
TabPFNRegressor.predict(
X: XType,
*,
output_type: Literal["full"],
quantiles: list[float] | None = None,
) -> FullOutputDict
Type aliases
XType = Any
OutputType = Literal["mean", "median", "mode", "quantiles", "full", "main"]
RegressionResultType = np.ndarray | list[np.ndarray] | MainOutputDict | FullOutputDict
| Parameter | Type | Default | Description |
|---|---|---|---|
X | X | Required | The input data. |
output_ | Output | "mean" | Determines the type of output to return. - If "mean", we return the mean over the predicted distribution.- If "median", we return the median over the predicted distribution.- If "mode", we return the mode over the predicted distribution.- If "quantiles", we return the quantiles of the predicted distribution. The parameter quantiles determines which quantiles are returned.- If "main", we return the all output types above in a dict.- If "full", we return the full output of the model, including the logits and the criterion, and all the output types from “main”. |
quantiles | list[float] | None | None | The quantiles to return if output="quantiles".By default, the [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9] quantiles are returned. The predictions per quantile match the input order. |
| Type | Description |
|---|---|
Regression | The prediction, which can be a numpy array, a list of arrays (for quantiles), or a dictionary with detailed outputs. |
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