Michael Mayer tries out a neural network model:
Tabular data has had a comfortable life for years. Gradient boosting showed up, got very good at its job, and then quietly became the default answer to almost everything with rows and columns.
In very recent years, a new player has arrived: the tabular foundation model or prior fitted neural network, and suddenly tabular data is sounding a lot less sleepy…
I’ve done a bit with TabPFN and come away fairly impressed. I’ll have to give this a go as well. There are definite limitations to data sizes before things fall over, but for moderate sizes (50k or fewer rows), TabPFN at least worked pretty well.