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Average performance for NNR models built by filter approach.
Average performance for NNR models built by filter approach.
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Figshare
2020-11-20 更新
2026-04-28 收录
特征选择
回归评估
数据链接:
https://figshare.com/articles/dataset/Average_performance_for_NNR_models_built_by_filter_approach_/13268336
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资源简介:
Average performance for NNR models built by filter approach.
应用场景:
创建时间:
2020-11-20
相关数据集
Straight line, 4D AND, 5D XOR, Hypersphere, Cone, Trigonometric, Double Spiral, Yin-yang, 5 multi-cut, 10 multi-cut
特征选择
数据集成
用于基准测试特征选择算法的合成数据集,包括多种类型的数据集,如直线、4D AND、5D XOR等,每个数据集都有其特定的特征和目标变量方程。
github
2024-05-23 更新
15
0
Regression model results for each pro-environmental behavior.
亲环境行为研究
回归评估
Regression model results for each pro-environmental behavior.
Figshare
2023-07-05 更新
4
0
Hyperparameter optimization using 10-fold grid search CV for the filtered LMCH data dynamics with 80:20 partition with feature selection.
参数优化
特征选择
Hyperparameter optimization using 10-fold grid search CV for the filtered LMCH data dynamics with 80:20 partition with feature selection.
Figshare
2024-05-16 更新
4
0
The highest classification accuracy of each algorithm on each data set and its feature subset length.
机器学习
特征选择
The highest classification accuracy of each algorithm on each data set and its feature subset length.
NIAID Data Ecosystem
8
0
Regression coefficient was obtained by L1-penalized logistic regression (details are described in Material and Methods) and sorted by descending absolute values.
L1正则化
特征选择
Elastic-net parameters alpha was set to 0.975 and lambda was estimated as 0.075. Coefficients were calculated with standardized variables (STDV = 1).
NIAID Data Ecosystem
4
0
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