SRAE: evidence-screened empirical-Bayes additive models
SRAE: evidence-screened empirical-Bayes additive models
The model is restricted to smooth main effects and selected pairwise interactions; no three-way or higher terms.
Interaction discovery is greedy and conditional on the main-effect fit. The screening score is a conditional residual marginal likelihood, not an exact Bayes factor for the full model — especially under the logistic likelihood.
The product-correlation pre-ranking used when the candidate set exceeds max_screen_pairs can miss interactions that are symmetric, masked, or poorly represented by a centered product.
Uncertainty summaries condition on the estimated hyperparameters, the selected interaction set, and the fixed basis. They do not propagate interaction-selection uncertainty.
Multiclass classification is one-vs-rest, not a jointly estimated multinomial model; the row-normalized probabilities are not guaranteed calibrated.
The logistic path is approximate (Laplace); evidence monotonicity is not guaranteed by the Gaussian EM argument.
The implementation uses dense linear algebra and does not accept sparse input, missing values, or non-numeric columns.