regressor

TabPFNRegressor class.

Example

import sklearn.datasets
from tabpfn import TabPFNRegressor

model = TabPFNRegressor()
X, y = sklearn.datasets.make_regression(n_samples=50, n_features=10)

model.fit(X, y)
predictions = model.predict(X)

TabPFNRegressor

Bases: RegressorMixin, BaseEstimator

TabPFNRegressor class.

bardist_ instance-attribute

bardist_: FullSupportBarDistribution

The bar distribution of the target variable, used by the model.

config_ instance-attribute

config_: InferenceConfig

The configuration of the loaded model to be used for inference.

device_ instance-attribute

device_: device

The device determined to be used.

executor_ instance-attribute

executor_: InferenceEngine

The inference engine used to make predictions.

feature_names_in_ instance-attribute

feature_names_in_: NDArray[Any]

The feature names of the input data.

May not be set if the input data does not have feature names, such as with a numpy array.

forced_inference_dtype_ instance-attribute

forced_inference_dtype_: _dtype | None

The forced inference dtype for the model based on inference_precision.

inferred_categorical_indices_ instance-attribute

inferred_categorical_indices_: list[int]

The indices of the columns that were inferred to be categorical, as a product of any features deemed categorical by the user and what would work best for the model.

interface_config_ instance-attribute

interface_config_: ModelInterfaceConfig

Additional configuration of the interface for expert users.

n_features_in_ instance-attribute

n_features_in_: int

The number of features in the input data used during fit().

n_outputs_ instance-attribute

n_outputs_: Literal[1]

The number of outputs the model supports. Only 1 for now

preprocessor_ instance-attribute

preprocessor_: ColumnTransformer

The column transformer used to preprocess the input data to be numeric.

renormalized_criterion_ instance-attribute

renormalized_criterion_: FullSupportBarDistribution

The normalized bar distribution used for computing the predictions.

use_autocast_ instance-attribute

use_autocast_: bool

Whether torch's autocast should be used.

y_train_mean_ instance-attribute

y_train_mean_: float

The mean of the target variable during training.

y_train_std instance-attribute

y_train_std: float

The standard deviation of the target variable during training.

fit

fit(X: XType, y: YType) -> Self

Fit the model.

Parameters:

Name	Type	Description	Default
X	XType	The input data.	required
y	YType	The target variable.	required

Returns:

Type	Description
Self	self

predict

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

Predict the target variable.

Parameters:

Name	Type	Description	Default
X	XType	The input data.	required
output_type	Literal['mean', 'median', 'mode', 'quantiles', 'full', 'main']	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 output_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".	'mean'
quantiles	list[float] | 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.	None

Returns:

Type	Description
ndarray | list[ndarray] | dict[str, ndarray] | dict[str, ndarray | FullSupportBarDistribution]	The predicted target variable or a list of predictions per quantile.