⚡ TabPFN: Foundation Model for Tabular Data ⚡

Semi-automatic feature engineering process using Language Models and your dataset descriptions. Based on the paper "LLMs for Semi-Automated Data Science: Introducing CAAFE for Context-Aware Automated Feature Engineering" by Hollmann, Müller, and Hutter (2023).

⚡ Easy API access to the tabular foundation model TabPFN ⚡

Zero-shot Time Series Forecasting with TabPFN (work accepted at NeurIPS 2024 TRL and TSALM workshops)

Community extensions for TabPFN - the foundation model for tabular data. Built with TabPFN! 🤗

Ensemble-based, size-agnostic wrapper for the TabPFN classifier

Code for finetuning TabPFN on one downstream tabular dataset.

TFG realizado en la Universidad de Burgos del desarrollo de una aplicación para el uso de un Radar de 60 GHz de la marca Acconeer.

Code for "TabMDA: Tabular Manifold Data Augmentation for Any Classifier using Transformers with In-context Subsetting" (ICML 2024)

TabPFN implementation with PyTorch

Code for evaluating TabPFN against other classifiers on engineering design problems.

PyTorch normalization layer that learns Yeo-Johnson power scaling on tabular data

ML competition submission to classify anonymous age related condition

Benchmarking the performance of TabPFNClassifier against traditional machine learning models (Logistic Regression, SVM, k-NN, Decision Tree, GaussianNB) on tabular datasets. Includes metrics like accuracy, fit time, and predict time, with interactive visualizations.