Pretty impressive, TabPFN immediately outperformed trees & NNs for a low-data binary classification task (2700 samples, 100 features).

The data comes from a large-scale evaluation of AI-generated science ideas, human experts evaluated interest-levels (The features are 100 properties of the graph that generated the research idea, the classes are high-interest level (ranked 4 or 5 out of 5) or low interest-level (1-3).

Both for NN and Tree we have previously performed some hyperparameter-adjustment. For TabPFN, i just pip-installed and run.