ArticlePacific Symposium on Biocomputing. Pacific Symposium on Biocomputing2018
Improving the explainability of Random Forest classifier - user centered approach.
Article in Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
18 citing papers in PubMed.
- Oligonucleotide Selective Detection by Levitated Optomechanics.ACS nanoscience Au · 2026Article
- Spatial intensity modelling of ISIS-related attacks using random forest regression: A GeoAI-based analysis of GTD data (2012-2019).PloS one · 2026Article
- Prioritization of patients at risk of heart attack using a novel full-objective ITARA based on Random Forest and Decision tree.Scientific reports · 2025Article
- Predicting ICU Delirium in Critically Ill COVID-19 Patients Using Demographic, Clinical, and Laboratory Admission Data: A Machine Learning Approach.Life (Basel, Switzerland) · 2025Article
- Enlightened prognosis: Hepatitis prediction with an explainable machine learning approach.PloS one · 2025Article
- Enhanced Hand Gesture Recognition with Surface Electromyogram and Machine Learning.Sensors (Basel, Switzerland) · 2024Article
- What distinguishes emotion-label words from emotion-laden words? The characterization of affective meaning from a multi-componential conception of emotions.Frontiers in psychology · 2024Article
- Article
- Predicting Heavy Metal Concentrations in Shallow Aquifer Systems Based on Low-Cost Physiochemical Parameters Using Machine Learning Techniques.International journal of environmental research and public health · 2022Article
- Anti-Ebola: an initiative to predict Ebola virus inhibitors through machine learning.Molecular diversity · 2022Article
- A Novel Integration of IF-DEMATEL and TOPSIS for the Classifier Selection Problem in Assistive Technology Adoption for People with Dementia.International journal of environmental research and public health · 2022Article
- Plasma Proteomics in Healthy Subjects with Differences in Tissue Glucocorticoid Sensitivity Identifies A Novel Proteomic Signature.Biomedicines · 2022Article
- Exploration of Biomarkers of Psoriasis through Combined Multiomics Analysis.Mediators of inflammation · 2022Article
- Clinician Preimplementation Perspectives of a Decision-Support Tool for the Prediction of Cardiac Arrhythmia Based on Machine Learning: Near-Live Feasibility and Qualitative Study.JMIR human factors · 2021Article
- Feature Selection and Validation of a Machine Learning-Based Lower Limb Risk Assessment Tool: A Feasibility Study.Sensors (Basel, Switzerland) · 2021Article
- DNA methylation-based profiling of bone and soft tissue tumours: a validation study of the 'DKFZ Sarcoma Classifier'.The journal of pathology. Clinical research · 2021Article
- Principles and Practice of Explainable Machine Learning.Frontiers in big data · 2021Review
- Prediction of slaughter age in pigs and assessment of the predictive value of phenotypic and genetic information using random forest.Journal of animal science · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Machine Learning (ML) methods are now influencing major decisions about patient care, new medical methods, drug development and their use and importance are rapidly increasing in all areas. However, these ML methods are inherently complex and often difficult to understand and explain resulting in barriers to their adoption and validation. Our work (RFEX) focuses on enhancing Random Forest (RF) classifier explainability by developing easy to interpret explainability summary reports from trained RF classifiers as a way to improve the explainability for (often non-expert) users. RFEX is implemented and extensively tested on Stanford FEATURE data where RF is tasked with predicting functional sites in 3D molecules based on their electrochemical signatures (features). In developing RFEX method we apply user-centered approach driven by explainability questions and requirements collected by discussions with interested practitioners. We performed formal usability testing with 13 expert and non-expert users to verify RFEX usefulness. Analysis of RFEX explainability report and user feedback indicates its usefulness in significantly increasing explainability and user confidence in RF classification on FEATURE data. Notably, RFEX summary reports easily reveal that one needs very few (from 2-6 depending on a model) top ranked features to achieve 90% or better of the accuracy when all 480 features are used.
Indexed as
Identifiers
29218882PMC5728671What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.