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Xgboost hyperparameter tuning result.
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创建时间：
2022-09-07
相关数据集
Image segmentation parameters and their corresponding values.
Image segmentation parameters and their corresponding values.
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Best neural network model features for predicting the different outcomes, determined by testing different combinations of activation functions, number of layers, and number of neurons per layer for each data set.
Best neural network model features for predicting the different outcomes, determined by testing different combinations of activation functions, number of layers, and number of neurons per layer for ea
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sj-zip-1-act-10.1177_1748302621999621 - Supplemental material for Optimal parameter selection in Weeks’ method for numerical Laplace transform inversion based on machine learning
Supplemental material, sj-zip-1-act-10.1177_1748302621999621 for Optimal parameter selection in Weeks’ method for numerical Laplace transform inversion based on machine learning by Patrick O Kano, Moy
DataCite Commons2024-08-28 更新30
Hyperparameter ranges and optimal values for all classification models.
Hyperparameter ranges and optimal values for all classification models.
Figshare2025-07-21 更新80
Supplementary Material(dataset).zip
The dataset contains the data used in each parameter graph (Fig.3-Fig.10), that is, the relationship between the change of each parameter and the various properties (such as birefringence, loss, PER,
DataCite Commons2024-10-22 更新40
