I'm excited to announce the release of our new Tabular Foundation Model TabPFN-3.5!
It improves a lot on standard tabular prediction problems (rank 1 by far [TabArena](https://github.com/autogluon/tabarena) and much faster than the competing models, 98% win rate against a well tuned XGBoost). But most importantly it really improves on the messy real-world data we've seen across industries. Be it non-i.i.d. data with temporal or grouped splits, tables with strings, text and images, high-cardinality categorical features, or wide tables with many features, we're also state of the art. Inside our harness, it's also the best model to predict on relational data, outperforming specific graph-based models.
Would love to hear how it performs on your own data!
I'm excited to announce the release of our new Tabular Foundation Model TabPFN-3.5!
It improves a lot on standard tabular prediction problems (rank 1 by far [TabArena](https://github.com/autogluon/tabarena) and much faster than the competing models, 98% win rate against a well tuned XGBoost). But most importantly it really improves on the messy real-world data we've seen across industries. Be it non-i.i.d. data with temporal or grouped splits, tables with strings, text and images, high-cardinality categorical features, or wide tables with many features, we're also state of the art. Inside our harness, it's also the best model to predict on relational data, outperforming specific graph-based models.
Would love to hear how it performs on your own data!