Impact of hyper-parameter fine-tuning on model performance.

<p>The boxplot showcases the 15 ROC_AUC scores, comprising the 5 highest, 5 intermediate, and 5 lowest, for KNN, SVC, RF, and XGB. For each boxplot, the parameter configurations are ordered based on their median ROC_AUC scores, from highest to lowest.</p>

محفوظ في:

التفاصيل البيبلوغرافية
المؤلف الرئيسي:	Binghan Xue (21253235) (author)
مؤلفون آخرون:	Yanji Xu (185940) (author), Ruili Huang (480703) (author), Qian Zhu (191342) (author)
منشور في:	2025
الموضوعات:	Biochemistry Pharmacology Biotechnology Science Policy Biological Sciences not elsewhere classified Chemical Sciences not elsewhere classified Information Systems not elsewhere classified uncover latent relationships support vector classifier novel treatment modalities medical challenges necessitates innovative therapeutic solutions extreme gradient boosting effective therapeutic interventions edge computational techniques drug repurposing process drug repurposing emerges 30 million individuals developing predictive models 143 gene targets gene targets vitro assays ultimately catalyzing systematic prediction several findings risk mitigation random forest promising avenue primarily focused previous investigation nearest neighbors harness cutting endeavor expanded diverse algorithms building upon 75 ),
الوسوم:	إضافة وسم لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!

Impact of hyper-parameter fine-tuning on model performance.

مواد مشابهة