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Comparison of AUC (%) of proposed model with DenseNet121.

NIAID Data Ecosystem2026-05-02 收录

分类器性能评估

深度学习模型比较

数据链接：

https://figshare.com/articles/dataset/Comparison_of_AUC_of_proposed_model_with_DenseNet121_/28609611 数据链接链接失效反馈

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资源简介：

Comparison of AUC (%) of proposed model with DenseNet121.

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创建时间：

2025-03-17

随机森林参数调优

分类器性能评估

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

NIAID Data Ecosystem60

Leave-one-out cross-validation results.

分类器性能评估

自学习分类器

Recall, precision, and f-measure are calculated for each class. Weighted f-measure, fW, and average accuracy, Acc, are calculated for all classes of a problem. All assessments are based on leave-one-o

NIAID Data Ecosystem50

A battery of metrics of five classifiers in the training and validation cohorts for fivefold nested cross-validation.

分类器性能评估

交叉验证

A battery of metrics of five classifiers in the training and validation cohorts for fivefold nested cross-validation.

NIAID Data Ecosystem40

The Cost-Effectiveness of Reclassification Sampling for Prevalence Estimation

流行病学抽样方法

分类器性能评估

BackgroundTypically, a two-phase (double) sampling strategy is employed when classifications are subject to error and there is a gold standard (perfect) classifier available. Two-phase sampling involv

NIAID Data Ecosystem60

Performance comparison of supervised multi-class classifiers on internal and external testing sets.

分类器性能评估

监督与半监督学习模型评估

Class-weighted values are reported.

Figshare2020-03-10 更新40