radiomics-ml

# 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. ## When to use - You have a radiomics or clinical/tabular feature table and want to build a random-forest / XGBoost clinical prediction model that will pass statistical review. - You want to audit an existing radiomics/ML pipeline for the failure modes below. ## When NOT to use - Deep-learning imaging models → `/architecture-zoo` → `/model-scaffold` → `/model-validation`. - Classical inferential statistics / a regression model as the estimand → `/analyze-stats`. - Interpretability of a trained network → `/ex...