Radiomics / Classical-ML Skill

Purpose

Radiomics + tree-ensemble studies (features → random forest / XGBoost → a clinical outcome) are the most common solo-doable clinical-ML workflow — no GPU, no engineer — and the most commonly over-optimistic: hundreds-to-thousands of features on tens of patients, hyperparameters tuned on the same folds the performance is reported from, features selected on the whole dataset, unstable features never filtered, and discrimination (AUC) reported without calibration. This skill produces the pipeline correctly and audits an existing one, so the clinical result survives review (Lambin 2017; CLEAR; TRIPOD+AI; PROBAST-AI).

It sits beside the imaging-DL lane: where /model-scaffold builds a deep network, radiomics-ml covers the feature-based classical-ML path. It integrates scikit-learn / xgboost / pyradiomics (referenced in the emitted code); it does not reimplement them and never runs a model on real patient data.