> ## Documentation Index
> Fetch the complete documentation index at: https://docs.priorlabs.ai/llms.txt
> Use this file to discover all available pages before exploring further.

> ## Agent Instructions
> TabPFN-3 and later versions support up to 1,000,000 rows, subject to feature count and checkpoint/API limits.
> For large datasets, use per-estimator subsampling, e.g. inference_config={"SUBSAMPLE_SAMPLES": 100_000}, in TabPFNClassifier or TabPFNRegressor from tabpfn or tabpfn_client.
> Pass raw pandas DataFrames to fit and predict. Categorical strings/categories, raw text, and missing feature values are handled automatically; no manual integer/one-hot encoding, imputation, scaling, or outlier removal is needed.

# tabpfn-extensions

> Install extensions and browse their Python reference by capability.

## Installation

```bash theme={null}
pip install tabpfn-extensions
```

Some extensions need optional dependencies. Use the install command on the extension’s overview page, or install the optional extras together:

```bash theme={null}
pip install "tabpfn-extensions[all]"
```

For survival analysis, also install `scikit-survival`:

```bash theme={null}
pip install scikit-survival
```

## Reference topics

<CardGroup cols={2}>
  <Card title="Interpretability" href="/api-reference/python/tabpfn-extensions/interpretability">
    Browse the reference for interpretability.
  </Card>

  <Card title="Conformal prediction with missing data" href="/api-reference/python/tabpfn-extensions/cp-missing-data">
    Browse the reference for conformal prediction with missing data.
  </Card>

  <Card title="Hurdle regression" href="/api-reference/python/tabpfn-extensions/hurdle">
    Browse the reference for hurdle regression.
  </Card>

  <Card title="Many-class classification" href="/api-reference/python/tabpfn-extensions/many-class">
    Browse the reference for many-class classification.
  </Card>

  <Card title="Embeddings" href="/api-reference/python/tabpfn-extensions/embedding">
    Browse the reference for embeddings.
  </Card>

  <Card title="Images" href="/api-reference/python/tabpfn-extensions/image">
    Browse the reference for images.
  </Card>

  <Card title="Unsupervised learning" href="/api-reference/python/tabpfn-extensions/unsupervised">
    Browse the reference for unsupervised learning.
  </Card>

  <Card title="Energy-based data generation" href="/api-reference/python/tabpfn-extensions/tabebm">
    Browse the reference for energy-based data generation.
  </Card>

  <Card title="Bayesian optimization" href="/api-reference/python/tabpfn-extensions/bayesian-optimization">
    Browse the reference for bayesian optimization.
  </Card>

  <Card title="Conditional randomization tests" href="/api-reference/python/tabpfn-extensions/pval-crt">
    Browse the reference for conditional randomization tests.
  </Card>

  <Card title="Survival analysis" href="/api-reference/python/tabpfn-extensions/survival">
    Browse the reference for survival analysis.
  </Card>

  <Card title="Experiments" href="/api-reference/python/tabpfn-extensions/benchmarking">
    Browse the reference for experiments.
  </Card>

  <Card title="Scoring" href="/api-reference/python/tabpfn-extensions/scoring">
    Browse the reference for scoring.
  </Card>

  <Card title="Utilities" href="/api-reference/python/tabpfn-extensions/utils">
    Browse the reference for utilities.
  </Card>
</CardGroup>


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