Validation

TL;DR for operators A model reporting 99% accuracy creates an obvious decision pressure: should the team fund integration, start clinical workflow design, or treat the experiment as essentially solved? This paper is a useful example of why that decision cannot be made from the headline number alone. Its main results table reports a Stacking Classifier at 99.81% accuracy, Random Forest at 99.52%, and Bagging at 99.45%.1 But those results come from one public Kaggle dataset after the original 5,110 records—249 stroke-positive and 4,861 stroke-negative—were reduced to a balanced working dataset containing 249 observations in each class. ...

A demo is cheap. Ask an AI agent to build a web app, watch it spin up a cheerful interface, click a few buttons, and everyone briefly pretends software engineering has been solved. Then production begins. The app boots but stores nothing. The database schema exists but the handler quietly forgets foreign keys. The UI looks plausible until the first state transition. The test suite passes because it checked the page title, not the workflow. Somewhere, a dashboard reports “success.” Somewhere else, a user discovers the thing is an elegant cardboard storefront. ...