S4 Table -

NIAID Data Ecosystem2026-03-14 收录

分类器性能评估

三元与二元分类

数据链接：

https://figshare.com/articles/dataset/S4_Table_-/21733584 数据链接链接失效反馈

官方服务：

资源简介：

S4.1 Table. Ternary classifier AI model performance. S4.2 Table. Binary classification AI model performance. (ZIP)

创建时间：

2022-12-15

Results obtained using colour model combinations.

图像色彩特征分析

分类器性能评估

A) Average error of all classifiers and descriptors, B) ROC curves for Classification using AdaBoost and Bagging with intensity and CMYK&Hb&Lb&HSV&Lab colour model combination.

NIAID Data Ecosystem70

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

A comparison of the inter-annotator agreement and the classifier performance.

标注一致性

分类器性能评估

The inter-annotator agreement is computed from the examples labeled twice. The classifier performance is estimated from the 10-fold cross-validation.

Figshare2015-12-03 更新30

Additional file 2: of The parameter sensitivity of random forests

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

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