xaitalk/mlp-heart-disease

MLP — UCI Heart Disease (Cleveland) — xaitalk tabular demo

A small 4-layer MLP (13→64→32→16→2) trained once on the UCI Heart Disease (Cleveland) dataset, frozen and published so xaitalk's tabular adapter loads fixed, reproducible weights (via xaitalk.hub.ensure_model) across PyTorch / TensorFlow / JAX — rather than retraining on every load().

This makes the tabular domain deterministic (canonical + cross-device consistency) and, crucially, demonstrates XAI on real, interpretable clinical features (age, cholesterol, resting BP, ...) rather than synthetic data — there is no signal to attribute in random data.

• Weights: framework-agnostic numpy .npz (JAX layout; PT transposes Dense).
• Trainer: deterministic numpy Adam (seed 42), so the artifact is reproducible.
• Dataset: UCI Heart Disease (Cleveland), 303 samples, 13 features, 2 classes.

Part of xaitalk — cross-framework XAI.