每个机器学习和深度学习模型的超参数集

数据集获取（Figshare）

• 源站标题：The hyperparameters set for each ML and DL model.
• DOI：10.1371/journal.pone.0311250.t003
• 发布日期：2025-02-10
• 文件数：1 个
• 文件总体积：13.82 KB
• 源站页面：https://plos.figshare.com/articles/dataset/The_hyperparameters_set_for_each_ML_and_DL_model_/28383618

命令行下载

curl -L -o "Table 3.xls" "https://ndownloader.figshare.com/files/52246020"

文件清单

• Table 3.xls（13.82 KB）

文件总体积 13.82 KB（源站 API 实测）。

数据加载示例（图像类）

from PIL import Image
import glob, os

files = (glob.glob(os.path.join(path, "**", "*.png"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.jpg"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.tif"), recursive=True))
print("图像文件数:", len(files))
img = Image.open(files[0]); print("尺寸/模式:", img.size, img.mode)

# torchvision Dataset 方式:
# from torchvision import datasets
# ds = datasets.ImageFolder(path)  # 要求 子目录=类别

目录组织与标注格式以源站说明和下载后实际文件为准。

数据说明

The healthcare industry is generating a massive volume of data, promising a potential goldmine of information that can be extracted through machine learning (ML) techniques. The Intensive Care Unit (ICU) stands out as a focal point within hospitals and provides a rich source of data for informative analyses. This study examines the cardiac surgery ICU, where the vital topic of patient ventilation takes center stage. In other words, ventilator-supported breathing is a fundamental need within the ICU, and the limited availability of ventilators in hospitals has become a significant issue. A crucial consideration for healthcare professionals in the ICU is prioritizing patients who require ventilators immediately. To address this issue, we developed a prediction model using four ML and deep learning (DL) models—LDA, CatBoost, Artificial Neural Networks (ANN), and XGBoost—that are combined in an ensemble model. We utilized Simulated Annealing (SA) and Genetic Algorithm (GA) to tune the hyperparameters of the ML models constructing the ensemble. The results showed that our approach enhanced the sensitivity of the tuned ensemble model to 85.84%, which are better than the results of the ensemble model without hyperparameter tuning and those achieved using AutoML model. This significant improvement in model performance underscores the effectiveness of our hybrid approach in prioritizing the need for ventilators among ICU patients.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.1371/journal.pone.0311250.t003（source: https://plos.figshare.com/articles/dataset/The_hyperparameters_set_for_each_ML_and_DL_model_/28383618）。