mlr_learners_classif.tabpfn: TabPFN Classification Learner

In mlr-org/mlr3extralearners: Extra Learners For mlr3

TabPFN Classification Learner

Description

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

Installation

While the Python dependencies are handled via reticulate::py_require(), you can manually specify a virtual environment by calling reticulate::use_virtualenv() prior to calling the ⁠$train()⁠ function. In this virtual environment, the tabpfn package and its dependencies must be installed.

Saving a Learner

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

• 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" or "autocast". Passing torch.dtype is currently not supported.

• inference_config is currently not supported.

Dictionary

This Learner can be instantiated via lrn():

lrn("classif.tabpfn")

Meta Information

• Task type: “classif”

• Predict Types: “response”, “prob”

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

• Required Packages: mlr3

Parameters

Id	Type	Default	Levels	Range
n_estimators	integer	4		[1, \infty)
categorical_features_indices	untyped	-		-
softmax_temperature	numeric	0.9		[0, \infty)
balance_probabilities	logical	FALSE	TRUE, FALSE	-
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	-
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)

Super classes

mlr3::Learner -> mlr3::LearnerClassif -> LearnerClassifTabPFN

Active bindings

marshaled

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

Methods

Public methods

• LearnerClassifTabPFN$new()

• LearnerClassifTabPFN$marshal()

• LearnerClassifTabPFN$unmarshal()

• LearnerClassifTabPFN$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()

---

Method new()

Creates a new instance of this R6 class.

Usage

LearnerClassifTabPFN$new()

---

Method marshal()

Marshal the learner's model.

Usage

LearnerClassifTabPFN$marshal(...)

Arguments

...

(any)
Additional arguments passed to marshal_model().

---

Method unmarshal()

Unmarshal the learner's model.

Usage

LearnerClassifTabPFN$unmarshal(...)

Arguments

...

(any)
Additional arguments passed to unmarshal_model().

---

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerClassifTabPFN$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Author(s)

b-zhou

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. \Sexpr[results=rd]{tools:::Rd_expr_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.

Examples

# Define the Learner
learner = mlr3::lrn("classif.tabpfn")
print(learner)

# Define a Task
task = mlr3::tsk("sonar")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

mlr-org/mlr3extralearners documentation built on June 11, 2025, 7:06 p.m.