random_forest_differing_windows

Dryad2018-01-01 更新2026-04-13 收录

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随机森林参数优化

数据链接：

https://datadryad.org/resource/doi:10.5061/dryad.1s1m8r7/3 数据链接链接失效反馈

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资源简介：

r script for testing how different window sizes influences accuracy of classification using the random forest algorithm

创建时间：

2018-01-01

Additional file 2: of The parameter sensitivity of random forests

随机森林参数优化

低p/n数据预测

AUC results for low p/n data. Low p/n results for prediction accuracy using AUC as the performance metric for non-cross-validation results, 10-fold cross-validation and stratified 10-fold cross-valida

Figshare2016-12-15 更新90

Additional file 2: of The parameter sensitivity of random forests

随机森林参数优化

低p/n数据预测

Figshare2016-12-15 更新70

Additional file 6: of The parameter sensitivity of random forests

随机森林参数优化

显著性检测

Pairwise t-test results for m try intra-parameter groups for the low p/n data. All p-values were adjusted using a Benjamini-Hochberg procedure. Each sampsize group was found to differ from at least 12

NIAID Data Ecosystem50

Additional file 11: of The parameter sensitivity of random forests

随机森林参数优化

生物医学数据分析

Pairwise t-test results for m try intra-parameter groups for the combined NSCLC validation data. All p-values were adjusted using a Benjamini-Hochberg procedure. In general lower m try values were fou

NIAID Data Ecosystem90

Additional file 3: of The parameter sensitivity of random forests

随机森林参数优化

显著性检测

Pairwise t-test results for sampsize intra-parameter groups for the low p/n data. All p-values were adjusted using a Benjamini-Hochberg procedure. There were no groups that differed significantly from

Figshare2016-12-15 更新60

random_forest_differing_windows

资源简介：

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