{"slug":"aperivue-radiomics-ml","source_name":"aperivue/radiomics-ml","name":"Aperivue/Radiomics ML","description":"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 reg","version":1,"lift":{"pass_rate_delta_pts":27.27,"pass_rate_pct":86.4,"total_cases":22,"passed_cases":19,"tokens_delta_pct":31.4,"turns_delta_pct":0,"verdict":"mixed","benchmark_model":"gemini-3.6-flash","grading_method":"judged","completed_at":"2026-08-24T22:34:17.678208+00:00"},"skill_score":0.8636,"benchmark_models":[{"model":"gemini-3.6-flash","headline":true,"delta_pts":27.27,"with_pass_pct":86.4,"without_pass_pct":59.1,"tokens_delta_pct":31.4,"turns_delta_pct":0,"total_cases":22,"cases_aggregated":20,"verdict":"mixed","never_hurt":true,"completed_at":"2026-08-24T22:34:17.678208+00:00","run_id":"8ea5ad40-2099-4efd-ad35-24e571abb68a","version_number":1,"is_latest_version":true,"gate":null}],"trust":{"skill_safety":"passed","safety_status":"clean","intent_verdict":"safe","content_status":"clean","indexable":true},"license":"MIT","install_count":0,"manifest_hash":"0b464d70a0ed8fed90533e83c33c5ee62b6855c997cb1ca641ed22dbfe565689","raw_url":"https://app.decimal.ai/s/aperivue-radiomics-ml/SKILL.md","scorecard_url":"https://app.decimal.ai/skills/aperivue-radiomics-ml"}