XGBoost Design and Configuration with Hyperparameter Tuning.

NIAID Data Ecosystem2026-05-02 收录

参数优化

XGBoost模型优化

数据链接：

https://figshare.com/articles/dataset/XGBoost_Design_and_Configuration_with_Hyperparameter_Tuning_/29417966 数据链接链接失效反馈

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资源简介：

XGBoost Design and Configuration with Hyperparameter Tuning.

创建时间：

2025-06-26

Metaheuristic parameter tuning dateset

优化算法

参数优化

All thee metaheuristic algorithms (Ant Colony Optimization, Evolutionary Strategy, Imperialist Competitive Algorithm) contain multiple numerical parameters, that can be tuned to increase the efficienc

DataCite Commons2025-06-01 更新150

TWM Data Files

知识图谱嵌入

参数优化

Data files containing the full outputs of all hyperparameter searches needed for TWM to simulate the KGE model ComplEx on 5 KGs -- CoDExSmall, DBpedia50 OpenEA, Kinships, and UMLS. Data is in a pre-pr

NIAID Data Ecosystem80

Optimized MS/MS settings of purines and related metabolites.

嘌呤代谢物质谱分析

参数优化

Optimized MS/MS settings of purines and related metabolites.

NIAID Data Ecosystem60

ibm-research/rag-hpo-bench

检索增强生成

参数优化

该数据集名为RAG‑HPO Bench，包含了一篇关于检索增强生成（RAG）超参数优化方法分析的论文中的网格搜索结果。数据集涵盖了162种RAG配置在五个RAG QA数据集（AIArxiv、BioASQ、ClapNQ、MiniWiki和WatsonxQA）上的开发和测试分割的输出和评分。数据集还包括了不同配置的详细结果文件，探索的超参数空间（如块大小、块重叠、嵌入模型、Top-k和生成模型），以及

Hugging Face2026-01-19 更新120

Fractional Cross-Validation for Optimizing Hyperparameters of Supervised Learning Algorithms

机器学习

参数优化

K-fold cross-validation (CV) is a robust method for estimating generalization performance of supervised learning models. Although CV is more reliable than using a single hold-out test set, it is also

DataCite Commons2025-07-29 更新70

XGBoost Design and Configuration with Hyperparameter Tuning.

资源简介：

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