> ## 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.
# Experiments
> Base class for experiments. Experiments should be reproducible, i.e. the settings should give all the information needed to run the experiment. Experiments should be deterministic, i.e. the same settings should always give the same results.
## Installation
```bash theme={null}
pip install tabpfn-extensions matplotlib
```
Base class for experiments.
Experiments should be reproducible, i.e. the settings should give all the information
needed to run the experiment.
Experiments should be deterministic, i.e. the same settings should always give the same results.
```python theme={null}
Experiment(
task_type,
**kwargs,
)
```
**Parameters**
| Parameter | Type | Default | Description |
| - | - | - | - |
| task\_type | — | Required | — |
| \*\*kwargs | — | — | — |
***
***
Runs the experiment.
Should set self.results
```python theme={null}
Experiment.run(
tabpfn,
**kwargs,
)
```
**Parameters**
| Parameter | Type | Default | Description |
| - | - | - | - |
| tabpfn | — | Required | — |
| \*\*kwargs | — | — | — |
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