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Information Gain Filtration Demo Code Data
Information Gain Filtration Demo Code Data
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Figshare
2021-07-04 更新
2026-04-28 收录
特征选择
信息增益
数据链接:
https://figshare.com/articles/dataset/Information_Gain_Filtration_Demo_Code_Data/14905662
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资源简介:
Data for Information Gain Filtration Github Code Demonstration.
应用场景:
创建时间:
2021-07-04
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
The frequencies of features in the optimal feature subsets.
特征选择
特征重要性评估
The frequencies of features in the optimal feature subsets.
Figshare
2015-12-03 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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