radiomics-ml

radiomics-ml is a Model training & evaluation skill for AI agents, published by Aperivue in the medsci-skills repository on GitHub. Skillspace rates radiomics-ml C (71/100) and it passed the safety scan; the rating is based on static analysis only.

Produce or audit a radiomics / tabular clinical-ML study — imaging or clinical features → any classical learner (penalised logistic [LASSO / ridge / elastic-net], SVM, k-NN, naive Bayes, LDA/QDA, decision tree, random forest, gradient boosting [XGBoost / LightGBM / CatBoost], shallow MLP, stacked ensembles) → a clinical outcome — so it clears the rigor bar reviewers expect: nested cross-validation (tuning never on the reported folds), dimensionality control for the features-far-exceed-events regime, feature selection inside the fold, feature-stability (ICC / test-retest) filtering, calibration, and external/temporal validation. The deterministic gate is learner-agnostic (it audits the pipeline, not the algorithm). Emits a pipeline manifest and the gate. The most common solo-doable clinical-ML workflow — no GPU, no engineer. Integrates scikit-learn / xgboost / lightgbm / catboost / pyradiomics; it does not reimplement them.

Grade

C

Quality

71 / 100

Safety

Passed

GitHub stars

201

Unproven: rated by static analysis only, not yet compared head-to-head.

This rating describes the version of the repository we crawled; it may have changed since, so go by its current files.

Skillspace indexes and rates this skill; it does not host or endorse it. Run third-party skills at your own risk.