TabPFN Regression Learner

Foundation model for tabular data. Uses reticulate to interface with the tabpfn Python package.

Installation

This learner relies on reticulate to handle Python dependencies. It is not necessary to install any Python package manually in advance or specify a Python environment via reticulate::use_python(), reticulate::use_virtualenv(), reticulate::use_condaenv(), or reticulate::use_miniconda(). By calling $train() or $predict(), the required Python packages (tapfn, torch, etc.) will be installed automatically, if not already. Reticulate will then configure and initialize an ephemeral environment satisfying those requirements, unless an existing environment (e.g., "r-reticulate") in reticulate's Order of Discovery contains all the necessary packages.

You may also manually install tabpfn into a Python environment following the official installation guide and specify the environment via reticulate::use_*() before calling $train() or $predict(). Note that the GPU version of PyTorch cannot be loaded by reticulate::use_condaenv() from a conda environment. To use a conda environment, please install the CPU version of PyTorch.

Saving a Learner

In order to save a LearnerRegrTabPFN for later usage, it is necessary to call the $marshal() method on the Learner before writing it to disk, as the object will otherwise not be saved correctly. After loading a marshaled LearnerRegrTabPFN into R again, you then need to call $unmarshal() to transform it into a useable state.

Initial parameter values

• n_jobs is initialized to 1 to avoid threading conflicts with future.

Custom mlr3 parameters

• output_type corresponds to the same argument of the .predict() method of the TabPFNRegressor class, but only supports the options "mean", "median" and "mode". The point predictions are stored as $response of the prediction object.

• categorical_feature_indices uses R indexing instead of zero-based Python indexing.

• device must be a string. If set to "auto", the behavior is the same as original. Otherwise, the string is passed as argument to torch.device() to create a device.

• inference_precision must be "auto", "autocast", or a torch.dtype string, e.g., "torch.float32", "torch.float64", etc. Non-float dtypes are not supported.

• inference_config is currently not supported.

• random_state accepts either an integer or the special value "None" which corresponds to None in Python. Following the original Python implementation, the default random_state is 0.

Dictionary

This Learner can be instantiated via lrn():

lrn("regr.tabpfn")

Meta Information

• Task type: “regr”

• Predict Types: “response”, “quantiles”

• Feature Types: “logical”, “integer”, “numeric”

• Required Packages: mlr3, reticulate

Parameters

Id	Type	Default	Levels	Range
output_type	character	mean	mean, median, mode	-
n_estimators	integer	4		\([1, \infty)\)
categorical_features_indices	untyped	-		-
softmax_temperature	numeric	0.9		\([0, \infty)\)
average_before_softmax	logical	FALSE	TRUE, FALSE	-
model_path	untyped	"auto"		-
device	untyped	"auto"		-
ignore_pretraining_limits	logical	FALSE	TRUE, FALSE	-
inference_precision	character	auto	auto, autocast, torch.float32, torch.float, torch.float64, torch.double, torch.float16, torch.half, torch.bfloat16	-
fit_mode	character	fit_preprocessors	low_memory, fit_preprocessors, fit_with_cache	-
memory_saving_mode	untyped	"auto"		-
random_state	integer	0		\((-\infty, \infty)\)
n_jobs	integer	-		\([1, \infty)\)

References

Hollmann, Noah, Müller, Samuel, Purucker, Lennart, Krishnakumar, Arjun, Körfer, Max, Hoo, Bin S, Schirrmeister, Tibor R, Hutter, Frank (2025). “Accurate predictions on small data with a tabular foundation model.” Nature. doi:10.1038/s41586-024-08328-6 , https://www.nature.com/articles/s41586-024-08328-6.

Hollmann, Noah, Müller, Samuel, Eggensperger, Katharina, Hutter, Frank (2023). “TabPFN: A transformer that solves small tabular classification problems in a second.” In International Conference on Learning Representations 2023.

See also

• Dictionary of Learners: mlr3::mlr_learners.

• as.data.table(mlr_learners) for a table of available Learners in the running session (depending on the loaded packages).

• Chapter in the mlr3book: https://mlr3book.mlr-org.com/basics.html#learners

• mlr3learners for a selection of recommended learners.

• mlr3cluster for unsupervised clustering learners.

• mlr3pipelines to combine learners with pre- and postprocessing steps.

• mlr3tuning for tuning of hyperparameters, mlr3tuningspaces for established default tuning spaces.

Author

b-zhou

Super classes

mlr3::Learner -> mlr3::LearnerRegr -> LearnerRegrTabPFN

Active bindings

marshaled

(logical(1))
Whether the learner has been marshaled.

Methods

Public methods

• LearnerRegrTabPFN$new()

• LearnerRegrTabPFN$marshal()

• LearnerRegrTabPFN$unmarshal()

• LearnerRegrTabPFN$clone()

Inherited methods

• mlr3::Learner$base_learner()
• mlr3::Learner$configure()
• mlr3::Learner$encapsulate()
• mlr3::Learner$format()
• mlr3::Learner$help()
• mlr3::Learner$predict()
• mlr3::Learner$predict_newdata()
• mlr3::Learner$print()
• mlr3::Learner$reset()
• mlr3::Learner$selected_features()
• mlr3::Learner$train()
• mlr3::LearnerRegr$predict_newdata_fast()

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Method new()

Creates a new instance of this R6 class.

Usage

LearnerRegrTabPFN$new()

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Method marshal()

Marshal the learner's model.

Usage

LearnerRegrTabPFN$marshal(...)

Arguments

...

(any)
Additional arguments passed to mlr3::marshal_model().

---

Method unmarshal()

Unmarshal the learner's model.

Usage

LearnerRegrTabPFN$unmarshal(...)

Arguments

...

(any)
Additional arguments passed to mlr3::unmarshal_model().

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Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerRegrTabPFN$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.