> ## 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.

# Unsupervised learning

> Install the unsupervised extension and browse models, imputation, anomaly detection, and embeddings.

<Info>
  Looking for usage documentation? Check out [Anomaly detection](/capabilities/anomaly-detection) and [Data generation](/capabilities/data-generation).
</Info>

## Installation

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

## Reference topics

<CardGroup cols={2}>
  <Card title="Model and fitting" href="/api-reference/python/tabpfn-extensions/unsupervised/model">
    Configure and fit the unsupervised model.
  </Card>

  <Card title="Imputation and data generation" href="/api-reference/python/tabpfn-extensions/unsupervised/imputation">
    Impute missing values and generate data.
  </Card>

  <Card title="Anomaly detection" href="/api-reference/python/tabpfn-extensions/unsupervised/outliers">
    Calculate outlier scores.
  </Card>

  <Card title="Embeddings" href="/api-reference/python/tabpfn-extensions/unsupervised/embeddings">
    Extract embeddings from the unsupervised model.
  </Card>

  <Card title="Experiments" href="/api-reference/python/tabpfn-extensions/unsupervised/experiments">
    Unsupervised experiment utilities.
  </Card>
</CardGroup>


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