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Understanding Machine Learning Models
- Subjects: Computer Science, Artificial Intelligence
- |
- Contributor:
- Samuel Huang
- machine learning
- medical prediction
- bootstrap simulation
- shapely additive explanations
- SHAP
Video Introduction
This video is adapted from 10.1371/journal.pone.0281922
Machine learning methods are widely used within the medical field. However, the reliability and efficacy of these models are difficult to assess, making it difficult for researchers to identify which machine-learning model to apply to their dataset. The researchers of this study assessed whether variance calculations of model metrics (e.g., AUROC, Sensitivity, Specificity) through bootstrap simulation and SHapely Additive exPlanations (SHAP) could increase model transparency and improve model selection.
Find more at: https://scholar.google.com/citations?user=AwaT7tcAAAAJ&hl=en&oi=ao
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Huang, S. Understanding Machine Learning Models. Encyclopedia. Available online: https://encyclopedia.pub/video/video_detail/1492 (accessed on 05 December 2025).
Huang S. Understanding Machine Learning Models. Encyclopedia. Available at: https://encyclopedia.pub/video/video_detail/1492. Accessed December 05, 2025.
Huang, Samuel. "Understanding Machine Learning Models" Encyclopedia, https://encyclopedia.pub/video/video_detail/1492 (accessed December 05, 2025).
Huang, S. (2025, February 07). Understanding Machine Learning Models. In Encyclopedia. https://encyclopedia.pub/video/video_detail/1492
Huang, Samuel. "Understanding Machine Learning Models." Encyclopedia. Web. 07 February, 2025.
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