Show HN: TabPFN v2 – A SOTA foundation model for small tabular data

Author here! The breast cancer dataset is simple and heavily saturated, so small differences between methods are expected. As you say, single-use examples can be noisy due to randomness in how the data is randomly split into training and testing sets especially for a saturated dataset like this one. Cross-validation reduces this variance by averaging over multiple splits. I just ran this below:

  TabPFN mean ROC AUC: 0.9973

  SVM mean ROC AUC: 0.9903

  TabPFN per split: [0.99737963 0.99639699 0.99966931 0.99338624 0.99966465]

  SVM per split: [0.99312152 0.98788077 0.99603175 0.98313492 0.99128102]

  from sklearn.model_selection import cross_val_score
  from tabpfn import TabPFNClassifier
  from sklearn.datasets import load_breast_cancer
  from sklearn.svm import LinearSVC
  import numpy as np

  data = load_breast_cancer()
  X, y = data.data, data.target

  # TabPFN
  tabpfn_clf = TabPFNClassifier()
  tabpfn_scores = cross_val_score(tabpfn_clf, X, y, cv=5, 
  scoring='roc_auc')
  print("TabPFN per split:", tabpfn_scores)
  print("TabPFN mean ROC AUC:", np.mean(tabpfn_scores))
  
  # SVM
  svm_clf = LinearSVC(C=0.01)
  svm_scores = cross_val_score(svm_clf, X, y, cv=5, 
  scoring='roc_auc')
  print("SVM per split:", svm_scores)
  print("SVM mean ROC AUC:", np.mean(svm_scores))

TabPFN is better on numerical data since v1 (see figure 6 in the CARTE paper). CARTE's main strength in on text features, which are now also supported for TabPFN v2 API version (https://github.com/PriorLabs/tabpfn-client). We compared this to CARTE and found our model to be generally quite better, and much faster. CARTE multi-table approach is also very interesting, and we want to tackle this setting in the future.

Author here!

Thanks :)

1. Only for the first version, not for this version. I am sorry! 2. Yeah ours is guaranteed ok, as we wrote code to generate it basically just from plain torch ops. The code to run inference is available, just not the training code and data generation. 3. We have put it to work on time series data, which is very business relevant for example https://github.com/liam-sbhoo/tabpfn-time-series, and we have a table in the Appendix with all datasets we evaluate on in our main analysis to give you some ideas for possible datasets.

Thanks a lot! Currently have an issue on documenting how to use for more samples at https://github.com/PriorLabs/TabPFN/issues/129. Will do this soon, maybe give an upvote there if it matters to you.

Looks like a great use case! We have a method specifically for imputation in the tabpfn-extensions package (https://github.com/PriorLabs/tabpfn-extensions/blob/dbc3f5da...). It needs some cleaning up before I want to highlight in the notebooks and docs.

> 200 voters on 50 statements would fall within the 10,000 sample threshold.

I think you misinterpreted. 1 voter on 50 statements with (+1/-1/0) would be 1 datapoint with 50 features. 200 voters would be 200 rows with 50 features so you would not need to be concerned about the 10,000 sample threshold. Hope that helps your study.

Wow! Didn't expect the models to do so well on time series as well, will try this out.

Thanks a lot! We don't see clear artifacts for the synth data. Part of the "trick" is to keep the capacity of our model low, it has only about 11M parameters. That forces the model to "learn an in-context learning algorithm" or in other words "do in-context learning rather than in-weigthts learning". Adding real data on top will help, agreed! The synthetic data is very broad, we started by a synth data prior that was just BNNs samples with differing sizes and thus super broad. Our new data samples functions more densely that are simpler to explain but could still sample almost any function (with the constraints that our networks aren't infinitely complex).

Author here: The new introduction of attention between features did make a big impact compared to the first variant of TabPFN. The old model handled every feature like it was completely different to be feature 5 vs 15, but actually features are typically more-or-less permutation invariant. So the logic is similar to why a CNN is better for images than an MLP.

Speculating, cross-row might give you information where you are in that row distribution.

Benchmark of tabpfn<2 compared to xgboost, lightgbm, and catboost: https://x.com/FrankRHutter/status/1583410845307977733 .. https://news.ycombinator.com/item?id=33486914

Yes it actually is but the limitations of rows and features could be a hindrance.

To put it very simply, the trick is that while the others train a new model for each problem, TabPFN is pre-trained to handle any kind of problem on the fly.

To draw a parallel to NLP: previously people trained a neural network for each kind of text classification they wanted to do, but then LLMs came around that pre-trained to learn to perform new tasks on the fly. Similarly, TabPFN learns to do new tasks on the fly just from the context (dataset) given.

Training and prediction in these models is by default one and the same, similar to how the prediction of the next token in an LLM is not split into learning from context and then doing the actual prediction. There is a way to split this even up, though, then the predictions, I believe, take something like 1/10s for medium-sized datasets.

thanks a ton! If it's public please share in the Discord https://discord.com/channels/1285598202732482621/ > #use-cases (just created!), if not, mail me at noah@priorlabs.ai

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Yes! This makes sense from a learning perspective: More samples add additional evidence the datapoint is actually what you observed - based on one sample the model is closer to a mean regression (which would translate to more balanced class probabilities in classification). Transformers have trouble counting repeated entries (there was a famous failure case of ChatGPT, asking it to count the number of 1s and 0s in a string). This model has some tricks to solve this.

Author here! The fundamental challenge is that LLMs like O1 and Claude 3.5 simply aren't built for the unique structures of tabular data. When processing tables through LLMs, the inefficiencies quickly become apparent - tokenizing a 10,000 x 100 table as a sequence and numerical values as tokens creates massive inefficiencies.

There's some interesting work on using LLMs for tabular data (TabLLM: https://proceedings.mlr.press/v206/hegselmann23a.html), but this only works for datasets with tens of samples rather than the thousands of rows needed in real-world applications.

What o1 and other LLMs typically do is wrap around existing tabular tools like XGBoost or scikit-learn. While this works, they're ultimately constrained by these tools' limitations. We're taking a fundamentally different approach - building foundation models that natively understand tabular relationships and patterns. Our approach combines the benefits of foundation models with architectures specifically designed for tabular data structures.

Tuned per dataset

Yes, there are normalizations applied before the features are fed to the neural network. Additionally, the neural network is trained on a very diverse set of artificial datasets.

There have been a ton of improvements! Much better performance overall, way larger data size limit (1K-->10K rows, 100-->500 features), regression support, native categorical data and missing values handling, much better support for uninformative or outlier features etc.

No, it is *much* stronger, a different architecture and scales to 10x the number of examples. It can also do regression now, and handle categorical features. Please, have a quick look at the abstract before making such claims.