Models

Prediction

• sklearn: lr, rf, knn, xgb, tabnet, tabpfn, mlp-sklearn

• lightning: mlp, ft-transformer

Generation

• real (passthrough)

• imblearn: smote

• tabeval: ctgan, tvae, bn, goggle, tabddpm, arf, nflow, great

Selecting parameters

• Each model class defines default, single-run, test, and Optuna search spaces via define_params.

• Lightning models further have architecture and optimization subsections.

Caveats

• Some generators are unstable on large datasets (see unstable_generator_list in src/tabstruct/common/__init__.py).

• Regression requires random splits; stratified is classification-only.