Contribute to TabPFN Cookbook
Bayesian Optimization with TabPFN Extensions
Use TabPFN as a Bayesian optimization surrogate via tabpfn-extensions
Experiment with Thinking Mode
Improve results of target metrics with TabPFN Thinking Mode
Faster Inference with KV Cache
Get a quick introduction into faster inference with fit_with_cache
Forecast Daily Spare-parts Demand with TabPFN
Compare local and pooled TabPFN regressors on synthetic demand from German
Generate Synthetic Data Using Real Data with TabPFN
Sample new rows that mirror the statistics of an existing table
Get Started with Predictive Distribution
Learn how to extract predictive distributions with TabPFNRegressor
Insurance Claim Modeling: TabPFN vs. GLM
Predict insurance pure premiums on zero-inflated claims data; TabPFN
Interpreting Results with TabPFN
Learn how to use the standard interpretability toolkit with TabPFN for
Interpreting Time-Series Forecasts with TabPFN-TS
Explain what drives a TabPFN-TS forecast with partial dependence, Window
Predictions over Relational Data with TabPFN-Rel
Get started with RelArena & TabPFN-Rel, a TabPFN Harness
See Which Training Rows Drive a TabPFN Prediction
Interpret TabPFN predictions as weighted votes from training rows with
TabPFN vs XGBoost
Compare TabPFN results with XGBoost using an OpenML Dataset
TabPFN with MLflow
Learn how to wrap TabPFN as an MLflow PythonModel, register it to Unity Catalog, and deploy it to a Mosaic AI Model serving endpoint.