<p>Optuna-based hyperparameter tuning: ranges and best parameters.</p>

NIAID Data Ecosystem2026-05-10 收录

数据链接：

https://figshare.com/articles/dataset/_p_Optuna-based_hyperparameter_tuning_ranges_and_best_parameters_p_/32026340 数据链接链接失效反馈

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资源简介：

Optuna-based hyperparameter tuning: ranges and best parameters.

创建时间：

2026-04-15

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

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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

Pixel intensity statistics after running background subtraction algorithm with variable window sizes.

背景减法算法

参数优化

The window size in FRET-IBRA defines the number of tiles the image width should be divided into, i.e., for a 640x480 pixel image, a window size set at 40 results in 40 windows along the width and 30 w

NIAID Data Ecosystem40

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

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Hyperparameter optimization using 10-fold grid search CV for the filtered LMCH data dynamics using a 70:30 partition with feature selection.

参数优化

网格搜索交叉验证

Hyperparameter optimization using 10-fold grid search CV for the filtered LMCH data dynamics using a 70:30 partition with feature selection.

Figshare2024-05-16 更新70