Fit a TabPFN model.

tab_pfn() applies data to a pre-estimated deep learning model defined by Hollmann et al (2025). This model emulates Bayesian inference for regression and classification models.

Usage

tab_pfn(x, ...)

# Default S3 method
tab_pfn(x, ...)

# S3 method for class 'data.frame'
tab_pfn(
  x,
  y,
  num_estimators = 8L,
  softmax_temperature = 0.9,
  balance_probabilities = FALSE,
  average_before_softmax = FALSE,
  training_set_limit = 10000,
  control = control_tab_pfn(),
  ...
)

# S3 method for class 'matrix'
tab_pfn(
  x,
  y,
  num_estimators = 8L,
  softmax_temperature = 0.9,
  balance_probabilities = FALSE,
  average_before_softmax = FALSE,
  training_set_limit = 10000,
  control = control_tab_pfn(),
  ...
)

# S3 method for class 'formula'
tab_pfn(
  formula,
  data,
  num_estimators = 8L,
  softmax_temperature = 0.9,
  balance_probabilities = FALSE,
  average_before_softmax = FALSE,
  training_set_limit = 10000,
  control = control_tab_pfn(),
  ...
)

# S3 method for class 'recipe'
tab_pfn(
  x,
  data,
  num_estimators = 8L,
  softmax_temperature = 0.9,
  balance_probabilities = FALSE,
  average_before_softmax = FALSE,
  training_set_limit = 10000,
  control = control_tab_pfn(),
  ...
)

Arguments

x

Depending on the context:

A data frame of predictors.
A matrix of predictors.
A recipe specifying a set of preprocessing steps created from recipes::recipe().

...

Not currently used, but required for extensibility.

y

When x is a data frame or matrix, y is the outcome specified as:

A data frame with 1 numeric column.
A matrix with 1 numeric column.
A numeric vector for regression or a factor for classification.

num_estimators

An integer for the ensemble size. Default is 8L.

softmax_temperature

An adjustment factor that is a divisor in the exponents of the softmax function (see Details below). Defaults to 0.9.

balance_probabilities

A logical to adjust the prior probabilities in cases where there is a class imbalance. Default is FALSE. Classification only.

average_before_softmax

A logical. For cases where num_estimators > 1, should the average be done before using the softmax function or after? Default is FALSE.

training_set_limit

An integer greater than 2L (and possibly Inf) that can be used to keep the training data within the limits of the data constraints imposed by the Python library.

control

A list of options produced by control_tab_pfn().

formula

A formula specifying the outcome terms on the left-hand side, and the predictor terms on the right-hand side.

data

When a recipe or formula is used, data is specified as:

A data frame containing both the predictors and the outcome.

Value

A tab_pfn object with elements:

fit: the python object containing the model.
levels: a character string of class levels (or NULL for regression)
training: a vector with the training set dimensions.
logging: any R or python messages produced by the computations.
blueprint: am object produced by hardhat::mold() used to process new data during prediction.

Details

Computing Requirements

This model can be used with or without a graphics processing unit (GPU). However, it is fairly limited when used with a CPU (and no GPU). There might be additional data size limitation warnings with CPU computations, and, understandably, the execution time is much longer. CPU computations can also consume a significant amount of system memory, depending on the size of your data.

GPUs using CUDA (Compute Unified Device Architecture) are most effective. Limited testing with others has shown that GPUs with Metal Performance Shaders (MPS) instructions (e.g., Apple GPUs) have limited utility for these specific computations and might be slower than the CPU for some data sets.

License Requirements

On November 6, 2025, PriorLabs released version 2.5 of the model, which contained several improvements. One other change is that accessing the model parameters required an API key. Without one, an error occurs:

"This model is gated and requires you to accept its terms. Please follow these steps: 1. Visit https://huggingface.co/Prior-Labs/tabpfn_2_5 in your browser and accept the terms of use. 2. Log in to your Hugging Face account via the command line by running: hf auth login (Alternatively, you can set the HF_TOKEN environment variable with a read token)."

The license contains provisions for "Non-Commercial Use Only" usage if that is relevant for you.

To get an API key, use the huggingface link above, create an account, and then get an API key. Once you have that, put it in your .Renviron file in the form of:


HF_TOKEN=your_api_key_value

The usethis function edit_r_environ() can be very helpful here.

Python Installation

You will need a working Python virtual environment with the correct packages to use these modeling functions.

There are at least two ways to proceed.

Ephemeral `uv` Install

The first approach, which we strongly suggest, is to simply load this package and attempt to run a model. This will prompt reticulate to create an ephemeral environment and automatically install the required packages. That process would look like this:


  > library(tabpfn)
  >
  > predictors <- mtcars[, -1]
  > outcome <- mtcars[, 1]
  >
  > # XY interface
  > mod <- tab_pfn(predictors, outcome)
  Downloading uv...Done!
  Downloading cpython-3.12.12 (download) (15.9MiB)
   Downloading cpython-3.12.12 (download)
  Downloading setuptools (1.1MiB)
  Downloading scikit-learn (8.2MiB)
  Downloading numpy (4.9MiB)

  <downloading and installing more packages>

   Downloading llvmlite
   Downloading torch
  Installed 58 packages in 350ms
  > mod
  TabPFN Regression Model

  Training set
  i 32 data points
  i 10 predictors

The location of the environment can be found at tools::R_user_dir("reticulate", "cache").

See the documentation for reticulate::py_require() to learn more about this method.

Manually created `venv` Virtual Environment

Alternatively, you can use the functions in the reticulate package to create a virtual environment and install the required Python packages there. An example pattern is:


  library(reticulate)

  venv_name <- "r-tabpfn"    # exact name can be different
  venv_seed_python <-
    virtualenv_starter(">=3.11,<3.14") 

  virtualenv_create(
    envname = venv_name,
    python = venv_seed_python,
    packages = c("numpy", "tabpfn")
  )

Once you have that virtual environment installed, you can declare it as your preferred Python installation with use_virtualenv(). (You must do this before reticulate has initialized Python, i.e., before attempting to use tabpfn):


  reticulate::use_virtualenv("r-tabpfn")

Data

Be default, there are limits to the training data dimensions:

Version 2.0: number of training set samples (10,000) and, the number of predictors (500). There is an unchangeable limit to the number of classes (10).
Version 2.5: number of training set samples (50,000) and, the number of predictors (2,000). There is an unchangeable limit to the number of classes (10).

Predictors do not require preprocessing; missing values and factor vectors are allowed.

Calculations

For the softmax_temperature value, the softmax terms are:


exp(value / softmax_temperature)

A value of softmax_temperature = 1 results in a plain softmax value.

References

Hollmann, Noah, Samuel Müller, Lennart Purucker, Arjun Krishnakumar, Max Körfer, Shi Bin Hoo, Robin Tibor Schirrmeister, and Frank Hutter. "Accurate predictions on small data with a tabular foundation model." Nature 637, no. 8045 (2025): 319-326.

Hollmann, Noah, Samuel Müller, Katharina Eggensperger, and Frank Hutter. "Tabpfn: A transformer that solves small tabular classification problems in a second." arXiv preprint arXiv:2207.01848 (2022).

Müller, Samuel, Noah Hollmann, Sebastian Pineda Arango, Josif Grabocka, and Frank Hutter. "Transformers can do Bayesian inference." arXiv preprint arXiv:2112.10510 (2021).

Examples

predictors <- mtcars[, -1]
outcome <- mtcars[, 1]

if (is_tab_pfn_installed() & interactive()) {
 # XY interface
 mod <- tab_pfn(predictors, outcome)

 # Formula interface
 mod2 <- tab_pfn(mpg ~ ., mtcars)

 # Recipes interface
 if (rlang::is_installed("recipes")) {
  suppressPackageStartupMessages(library(recipes))
  rec <-
   recipe(mpg ~ ., mtcars) %>%
   step_log(disp)

  mod3 <- tab_pfn(rec, mtcars)
  mod3
 }
}