Supervised learning for predicting unknown modifying variables in pliable lasso
SCIENTIFIC REPORTS, cilt.16, sa.10200, ss.1-16, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 16 Sayı: 10200
- Basım Tarihi: 2026
- Doi Numarası: 10.1038/s41598-026-36854-y
- Dergi Adı: SCIENTIFIC REPORTS
- Derginin Tarandığı İndeksler: Scopus, Science Citation Index Expanded (SCI-EXPANDED), BIOSIS, Chemical Abstracts Core, MEDLINE, Directory of Open Access Journals
- Sayfa Sayıları: ss.1-16
- Açık Arşiv Koleksiyonu: AVESİS Açık Erişim Koleksiyonu
- Ondokuz Mayıs Üniversitesi Adresli: Evet
Özet
Accurate outcome prediction often requires modeling complex interactions between input features and context-specific modifiers. The pliable lasso is a flexible regression framework that integrates such modifiers into the prediction process. In many real-world applications, however, these modifiers are unobserved at test time and must be estimated. This study investigates the performance of eight supervised machine learning algorithms for estimating the modifier matrix Z in a pliable lasso model under a known-to-unknown scenario. The analysis considers both classification accuracy for modifier estimation and regression accuracy for the final response prediction, using simulated data and two relevant real-world datasets: the Superconductivity dataset and the Mice Protein Expression dataset. Results indicate that tree-based ensemble models (e.g., XGBoost, Random Forest, and Decision Tree) deliver superior modifier classification (AUC>0.99), while regularized models such as Lasso and Elastic Net achieve the best regression performance. The findings support a hybrid modeling approach in which tree-based classifiers estimate modifying variables, followed by regularized regression for accurate and interpretable predictions.