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The Data Generation capability extends TabPFN’s unsupervised modeling system to create realistic synthetic tabular datasets. By modeling feature dependencies and joint probability distributions, TabPFN can generate new samples that follow the same statistical structure as your original data - useful for augmentation, simulation, and masking sensitive data. Data generation example

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Getting Started

Install the unsupervised extension: 
pip install "tabpfn-extensions[unsupervised]"
Then, use the TabPFNUnsupervisedModel with a TabPFN classifier and regressor model to generate new data: 
from tabpfn_extensions import unsupervised
from tabpfn_extensions.unsupervised import experiments
from sklearn.datasets import load_breast_cancer
import torch
from tabpfn_extensions import TabPFNClassifier, TabPFNRegressor

# Load and prepare breast cancer dataset
df = load_breast_cancer(return_X_y=False)
X, y = df["data"], df["target"]
feature_names = df["feature_names"]

# Initialize TabPFN models
model_unsupervised = unsupervised.TabPFNUnsupervisedModel(
    tabpfn_clf=TabPFNClassifier(), 
    tabpfn_reg=TabPFNRegressor()
)

# Select features for synthetic data generation
# Example features: [mean texture, mean area, mean concavity]
feature_indices = [4, 6, 12]

# Run synthetic data generation experiment
experiment = unsupervised.experiments.GenerateSyntheticDataExperiment(
    task_type="unsupervised"
)

results = experiment.run(
    tabpfn=model_unsupervised,
    X=torch.tensor(X),
    y=torch.tensor(y),
    attribute_names=feature_names,
    temp=1.0,                     # Temperature parameter for sampling
    n_samples=X.shape[0] * 2,     # Generate twice as many samples as original data
    indices=feature_indices,
)

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How it Works

The data generation process leverages the same probabilistic modeling used in TabPFN’s unsupervised mode:
  • Each feature is modeled conditionally on the others.
  • The chain rule of probability is used to estimate the full joint distribution.
  • New samples are drawn using the learned conditional dependencies, controlled by a temperature parameter (temp) that influences variability and diversity.

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Use Cases

Synthetic data generation can be applied across a range of research and engineering tasks:
  • Data augmentation - expand limited datasets for training or validation.
  • Privacy-preserving analytics - create realistic datasets without exposing sensitive information.

Google Colab Example

Check out our Google Colab for a demo.