Interpretable ensemble learning for tumor-type prediction with a SHAP-based evaluation of CatBoost and voting classifiers
Abstract Accurate early-stage diagnosis of tumours is crucial for improving patient prognosis. Modern machine learning techniques provide advanced and effective tools to support this process. In this study, both base classifiers and ensemble models were compared in the task of predicting tumour type...
محفوظ في:
| المؤلفون الرئيسيون: | , , |
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| التنسيق: | Artigo |
| اللغة: | Inglês |
| منشور في: |
Nature Portfolio
2025-12-01
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| سلاسل: | Scientific Reports |
| الموضوعات: | |
| الوصول للمادة أونلاين: | https://doi.org/10.1038/s41598-025-31079-x |
| الوسوم: |
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