# Prior Labs

## Docs

- [Model Context Protocol](https://docs.priorlabs.ai/agentic/mcp.md): Connect AI tools to TabPFN using the Model Context Protocol (MCP) for natural language predictions on tabular data.
- [Setup Guide](https://docs.priorlabs.ai/agentic/setup-guide.md): Step-by-step instructions for connecting Claude, ChatGPT, Cursor, Codex CLI, and n8n to the TabPFN MCP server.
- [Tool Use](https://docs.priorlabs.ai/agentic/tool-use.md): Reference for all tools exposed by the TabPFN MCP server.
- [Databricks](https://docs.priorlabs.ai/agentic/tutorials/databricks.md): Build a customer churn prediction pipeline using an AI agent, TabPFN MCP, and Databricks Delta tables.
- [TabPFN Skill for Claude](https://docs.priorlabs.ai/agentic/tutorials/mcp-claude-skills.md)
- [n8n](https://docs.priorlabs.ai/agentic/tutorials/n8n.md): Build an n8n chat workflow that accepts CSV uploads and runs TabPFN predictions through MCP.
- [Quickstart](https://docs.priorlabs.ai/api-reference/getting-started.md): Authenticate, upload your first dataset, and make predictions using the Prior Labs API.
- [Run Predictions](https://docs.priorlabs.ai/api-reference/prediction/run-predictions.md): Run inference using a previously fitted TabPFN model. Upload your test dataset and specify the model ID from your previous `/v1/fit` call. The endpoint returns predicted probabilities or values depending on the task type.
- [Overview](https://docs.priorlabs.ai/api-reference/security.md): How Prior Labs protects your data and ensures enterprise-grade security.
- [Fit a Model](https://docs.priorlabs.ai/api-reference/training/fit-a-model.md): Uploads and fits a TabPFN model on your training data. The API automatically handles preprocessing and stores a reference to your trained context (not the model weights). You can use either a single dataset file (with the target column included) or separate feature and label files.
- [Best Practices](https://docs.priorlabs.ai/best-practices.md): This guide outlines best practices for getting the optimal fit and predict speed out of TabPFN
- [Anomaly Detection](https://docs.priorlabs.ai/capabilities/anomaly-detection.md): Detect rare and anomalous samples using TabPFN’s unsupervised extension.
- [Classification](https://docs.priorlabs.ai/capabilities/classification.md): Learn about TabPFN's classification capabilities.
- [Data Generation](https://docs.priorlabs.ai/capabilities/data-generation.md): Generate realistic synthetic tabular data with TabPFN in seconds.
- [Embeddings](https://docs.priorlabs.ai/capabilities/embeddings.md): Extract latent feature representations from TabPFN models.
- [Fine-Tuning](https://docs.priorlabs.ai/capabilities/fine-tuning.md): Adapt TabPFN's pretrained foundation model to your data with gradient-based fine-tuning.
- [Time Series Forecasting](https://docs.priorlabs.ai/capabilities/forecasting.md): Learn about TabPFN's time series forecasting capabilities
- [Interpretability](https://docs.priorlabs.ai/capabilities/interpretability.md): Explain TabPFN predictions with Shapley values, feature interactions, and partial dependence plots.
- [Regression](https://docs.priorlabs.ai/capabilities/regression.md): Learn about TabPFN's regression capabilities.
- [TabPFN with MLflow](https://docs.priorlabs.ai/cookbooks/mlflow.md): Learn how to wrap TabPFN as an MLflow PythonModel, register it to Unity Catalog, and deploy it to a Mosaic AI Model serving endpoint.
- [Hyperparameter Optimization](https://docs.priorlabs.ai/extensions/hpo.md): Automatic hyperparameter tuning for TabPFN.
- [Many Class Classifier](https://docs.priorlabs.ai/extensions/many-class.md): Learn about classification with TabPFN for large number of classes.
- [AutoTabPFN Ensembles](https://docs.priorlabs.ai/extensions/post-hoc-ensembles.md): Learn about AutoTabPFN post-hoc ensembles using AutoGluon.
- [RF-PFN](https://docs.priorlabs.ai/extensions/rf-pfn.md): Hybrid Decision Trees and Random Forests for TabPFN
- [FAQ](https://docs.priorlabs.ai/faq.md)
- [Accessing Model Weights](https://docs.priorlabs.ai/how-to-access-gated-models.md)
- [Improving Performance](https://docs.priorlabs.ai/improving-performance.md): Practical strategies to improve TabPFN performance beyond the default configuration.
- [Feature Engineering](https://docs.priorlabs.ai/improving-performance/feature-engineering.md): Encode domain knowledge into features that TabPFN cannot learn from raw columns alone.
- [Feature Selection](https://docs.priorlabs.ai/improving-performance/feature-selection.md): Reduce feature count to improve TabPFN's attention efficiency and predictive power.
- [Model Parameters](https://docs.priorlabs.ai/improving-performance/model-parameters.md): Tune TabPFN's softmax temperature, metric optimization, and class imbalance handling.
- [Preprocessing Transforms](https://docs.priorlabs.ai/improving-performance/preprocessing.md): Configure TabPFN's internal preprocessing pipeline for maximum ensemble diversity.
- [Microsoft Foundry](https://docs.priorlabs.ai/integrations/foundry.md): Access TabPFN in your secure Azure environment.
- [Amazon SageMaker](https://docs.priorlabs.ai/integrations/sagemaker.md): Guide to setting up TabPFN-2.5 on Amazon SageMaker.
- [Models](https://docs.priorlabs.ai/models.md): Compare the different versions of the TabPFN foundation model, from the latest TabPFN-2.6 to the original v1.
- [Overview](https://docs.priorlabs.ai/overview.md): A tabular foundation model that delivers strong predictions in seconds — no dataset-specific training required.
- [Quickstart](https://docs.priorlabs.ai/quickstart.md): Get started with TabPFN in minutes.
- [Energy](https://docs.priorlabs.ai/use-cases/energy.md)
- [Finance](https://docs.priorlabs.ai/use-cases/finance.md)
- [Healthcare](https://docs.priorlabs.ai/use-cases/healthcare.md)
- [Industrials](https://docs.priorlabs.ai/use-cases/industrial.md)

## OpenAPI Specs

- [openapi](https://docs.priorlabs.ai/api-reference/openapi.json)

## Optional

- [GitHub](https://github.com/priorlabs/tabpfn)
- [Discord](https://discord.com/invite/VJRuU3bSxt)
- [Colab Demo](https://colab.research.google.com/github/PriorLabs/TabPFN/blob/main/examples/notebooks/TabPFN_Demo_Local.ipynb)