Classification pipeline and performance evaluation
REGRESSION
The feature group selection step can be used in combination with any classification model, but as a proof-of-concept, Logistic regression (LR) with LASSO regularisation was used here since the non-zero LR model coefficients can be used directly for model interpretation (they are equivalent to variable importance estimates [Two pipelines were evaluated: Conventional, using standard CFR with LR-LASSO and no feature group selection, and Proposed, using hierarchical CFR with feature group selection and LR-LASSO. In each case parameter tuning of the feature group selection and/or the LASSO regularisation coefficient was performed using a grid search with 3-fold CV, nested inside an outer 5-fold CV (repeated 100 times with different random seeds) for performance evaluation, see Fig. The ML pipelines and validation routines were implemented using the Python sklearn toolbox, and both CFR algorithms and the group feature selection step were implemented by extending the sklearn.base.BaseEstimator class.