Fitting and prediction

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`TabPFNRegressor.fit`

Fit the model. Uploads the training data to the server, which fits the model on it.

TabPFNRegressor.fit(
    X: pd.DataFrame | np.ndarray,
    y: pd.Series | np.ndarray,
    description: str | None = None,
)

Parameters

Parameter	Type	Default	Description
`X`	`pd.DataFrame \| np.ndarray`	Required	The input data.
`y`	`pd.Series \| np.ndarray`	Required	The target variable.
`description`	`str \| None`	`None`	Description of the dataset and task for the server.

Returns

Type	Description
—	self

Raises ValueError If the data or the estimator’s parameters are invalid, or the data exceeds the model limits.

`TabPFNRegressor.predict`

View source

Predict regression target for X.

TabPFNRegressor.predict(
    X: pd.DataFrame | np.ndarray,
    output_type: Literal["mean", "median", "mode", "quantiles", "full", "main"] = "mean",
    quantiles: list[float] | None = None,
) -> np.ndarray | list[np.ndarray] | dict[str, np.ndarray]

Parameters

Parameter	Type	Default	Description
`X`	`pd.DataFrame \| np.ndarray`	Required	The input data, of shape (n_samples, n_features).
`output_type`	`Literal[“mean”, “median”, “mode”, “quantiles”, “full”, “main”]`	`"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 all the output types above in a dict. - If `"full"`, we return the full output of the model, including the logits, the borders and all the output types from “main”. The criterion is included when the optional `tabpfn` and `torch` packages are installed.
`quantiles`	`list[float] \| None`	`None`	The quantiles to return if `output_type="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.

Returns

Type	Description
`np.ndarray \| list[np.ndarray] \| dict[str, np.ndarray]`	An array of predictions for `"mean"`, `"median"` and `"mode"`; a list with one array per quantile for `"quantiles"`; a dict of arrays for `"main"` and `"full"`.

Raises ValueError If X exceeds the model limits or the group columns are invalid. RuntimeError If a "full" prediction split across several requests returns fields that cannot be combined.

`TabPFNRegressor.get_timings`

View source

Seconds the server spent on the last fit and the last prediction. Returns {"fit": ..., "predict": ...}:

fit: elapsed_s, queue_wait_s, train_set_transform_s, fit_s.
predict: test_set_transform_queue_wait_s, test_set_transform_s, predict_queue_wait_s, predict_s. A prediction sent in several requests reports their sum.

Queue wait is time spent waiting for the server to start the work. An entry is None before the first call, on a model restored with load_model(), and when the server does not report timings.

TabPFNRegressor.get_timings() -> dict[str, dict[str, Any] | None]

Returns

Type	Description
`dict[str, dict[str, Any] \| None]`	—

`TabPFNRegressor.last_predict_timings`

View source

Seconds per stage of the most recent prediction, or None. Returns

Type	Description
`dict[str, Any] \| None`	—

Scikit-learn scoring

Inherited score(X, y, sample_weight=None) calls predict and returns the coefficient of determination (R²).

Reference

tabpfn

tabpfn-client

tabpfn-extensions

REST API

Fitting and prediction

`TabPFNRegressor.fit`

`TabPFNRegressor.predict`

`TabPFNRegressor.get_timings`

`TabPFNRegressor.last_predict_timings`

Scikit-learn scoring

​TabPFNRegressor.fit

​TabPFNRegressor.predict

​TabPFNRegressor.get_timings

​TabPFNRegressor.last_predict_timings

​Scikit-learn scoring

`TabPFNRegressor.fit`

`TabPFNRegressor.predict`

`TabPFNRegressor.get_timings`

`TabPFNRegressor.last_predict_timings`

Scikit-learn scoring