ArticleScientific reports2023
Differences in learning characteristics between support vector machine and random forest models for compound classification revealed by Shapley value analysis.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
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- Comprehensive analysis of metabolic and molecular alterations in the blood of patients with Sjögren's syndrome based on untargeted metabolomics analysis.BMC medical genomics · 2025Observational
- GCN-Based Framework for Materials Screening and Phase Identification.Materials (Basel, Switzerland) · 2025Article
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- Research progress on risk prediction models of physical restraint in the elderly: a narrative review.Frontiers in aging · 2025Review
- Robust colonoscopy polyp segmentation using dynamic-Nu T-Loss with multi-scale and uncertainty-aware adaptation.Frontiers in medicine · 2025Article
- Improving the explainability of autoencoder factors for commodities through forecast-based Shapley values.Scientific reports · 2024Article
- A Machine Learning-Based Mortality Prediction Model for Patients with Chronic Hepatitis C Infection: An Exploratory Study.Journal of clinical medicine · 2024Article
- Machine Learning Approaches Identify Chemical Features for Stage-Specific Antimalarial Compounds.ACS omega · 2023Article
- Deep Learning and The Retina: A New Frontier in Multiple Sclerosis Diagnosis.Current health sciences journalReview
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The random forest (RF) and support vector machine (SVM) methods are mainstays in molecular machine learning (ML) and compound property prediction. We have explored in detail how binary classification models derived using these algorithms arrive at their predictions. To these ends, approaches from explainable artificial intelligence (XAI) are applicable such as the Shapley value concept originating from game theory that we adapted and further extended for our analysis. In large-scale activity-based compound classification using models derived from training sets of increasing size, RF and SVM with the Tanimoto kernel produced very similar predictions that could hardly be distinguished. However, Shapley value analysis revealed that their learning characteristics systematically differed and that chemically intuitive explanations of accurate RF and SVM predictions had different origins.
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Registered trials
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