Welcome to TALENT, a benchmark with a comprehensive machine learning toolbox designed to enhance model performance on tabular data. TALENT integrates advanced deep learning models, classical algorithms, and efficient hyperparameter tuning, offering robust preprocessing capabilities to optimize learning from tabular datasets. The toolbox is user-friendly and adaptable, catering to both novice and expert data scientists.

TALENT offers the following advantages:

• Diverse Methods: Includes various classical methods, tree-based methods, and the latest popular deep learning methods.
• Extensive Dataset Collection: Equipped with 300 datasets, covering a wide range of task types, size distributions, and dataset domains.
• Customizability: Easily allows the addition of datasets and methods.
• Versatile Support: Supports diverse normalization, encoding, and metrics.

If you use any content of this repo for your work, please cite the following bib entries:

@article{ye2024closerlookdeeplearning,
         title={A Closer Look at Deep Learning on Tabular Data}, 
         author={Han-Jia Ye and 
         		 Si-Yang Liu and 
         		 Hao-Run Cai and 
         		 Qi-Le Zhou and 
         		 De-Chuan Zhan},
         journal={arXiv preprint arXiv:2407.00956},
         year={2024}
}

@article{JMLR:v26:25-0512,
  author  = {Si-Yang Liu and
			 Hao-Run Cai and
 			 Qi-Le Zhou and
			 Huai-Hong Yin and
			 Tao Zhou and
			 Jun-Peng Jiang and
			 Han-Jia Ye},
  title   = {Talent: A Tabular Analytics and Learning Toolbox},
  journal = {Journal of Machine Learning Research},
  year    = {2025},
  volume  = {26},
  number  = {226},
  pages   = {1--16},
  url     = {http://jmlr.org/papers/v26/25-0512.html}
}

• [2026-10]🌟 Add the September 2026 wave of tabular foundation models: TabPFN-3.5, LimiX-2, NVIDIA Kumo Tabular, Mitra-v2, Causilo, Xiaomi-TabLDM and EXAONE-Tabular. All foundation models now download their weights automatically and share one interface (n_estimators, sample_size); tabpfn_v3 now pins the TabPFN-3 checkpoint (tabpfn>=9.0 changed the package default to TabPFN-3.5).
• [2026-08]🌟 Add TRC (TPAMI 2026), a post-hoc representation corrector that trains and freezes an FT-Transformer backbone before learning representation re-estimation and light-space mapping.
• [2026-07]🌟 Add TabFM (Google Research, zero-shot tabular foundation model) as an optional method via pip install -U "tabfm[pytorch]".
• [2026-06]🌟 Add TabPFN v2.5 (PriorLabs, Nov 2025) and TabDPT (Layer 6 AI, ICL + retrieval). TALENT now ships the full TabPFN family (v1, v2, v2.5, v3) plus TabDPT alongside the other tabular foundation models.
• [2026-06]🌟 Evaluation improvements: calibration metrics (Brier, ECE) are now reported by default for every classifier, decision thresholds are tuned on the validation set for binary classification (used for Accuracy/F1/Precision/Recall; threshold-independent metrics like AUC/LogLoss/Brier/ECE are unaffected), and RunResult exposes a uniform predict_proba/predict_labels interface regardless of whether the underlying method natively returns logits or probabilities. Bundled checkpoints are now resolved via importlib.resources so methods work from any working directory.
• [2026-05]🌟 Add TabPFN v3 (PriorLabs 2026) and TabICL v2 (ICML 2026, regression support added).
• [2026-03]🌟 We have updated the TALENT-extension datasets and results. Link
• [2025-11]🌟 Add RFM (Science).
• [2025-11]🌟 Add Real-TabPFN.
• [2025-11]🌟 Add LimiX.
• [2025-09]🌟 Add xRFM.
• [2025-08]🌟 Add Mitra.
• [2025-06]🌟 Add TabAutoPNPNet (Electronics 2025).
• [2025-06]🌟 Add TabICL (ICML 2025). The current code is based on TabICL v0.1.2.
• [2025-05]🌟 Check out our three papers MMTU, Tabular-Temporal-Shift, and BETA accepted at ICML 2025!
• [2025-04]🌟 Check out our new survey