Multi-Algorithm Machine-Learning Feature Screening and Core-Feature Prioritization
Graphical abstract
Summary
Feature rankings and intersections from three machine-learning algorithms are cross-compared to establish model-level priorities for the core features. The graphical abstract integrates data distributions, model responses and feature comparisons across analytical layers. The results establish clear priorities for features, perturbations or biomarker candidates. The result-focused presentation supports efficient review of the main evidence and research priorities.
Computational results
Computational result 1
Figure 1: Through the machine learning screening center genes. A-B) LASSO regression algorithm. C-D) SVM-RFE algorithm; E-F) RF algorithm.; LASSO, the smallest absolute contraction and selection algorithm, supporting vector regression characteristic elimination; RF, random forest.
This figure presents the principal structures and trends in “” and connects them to the case-level ranking and result interpretation.
Full case PDF
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