---
license: mit
tags: [xaitalk, tabular, explainable-ai, 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](https://huggingface.co/xaitalk) — cross-framework XAI.