Create an `mlr3` classification learner backed by `tabpfn` through `reticulate`. This is a zero-shot tabular foundation model suited to small and medium tabular problems. TabPFN may require gated model access through Hugging Face depending on the selected model version.
Usage
make_tabpfn_learner(
model_version = NULL,
class_weights = "balanced",
device = c("auto", "cpu", "cuda"),
seed = 20260331L
)Arguments
- model_version
Optional TabPFN model version identifier exposed by `tabpfn.constants.ModelVersion`, for example `"V2_5"`. Default: `NULL`, which uses the package default.
- class_weights
Class weighting scheme. Included for interface consistency, but ignored by the current wrapper because TabPFN does not expose training-time class weighting here.
- device
`"auto"` (default), `"cpu"`, or `"cuda"`.
- seed
Random seed used inside the backend. Default: `20260331`.