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Performance of the classification models before applying dual-GAN.

NIAID Data Ecosystem2026-05-02 收录

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https://figshare.com/articles/dataset/Performance_of_the_classification_models_before_applying_dual-GAN_/28931603 数据链接链接失效反馈

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Performance of the classification models before applying dual-GAN.

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创建时间：

2024-09-27

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

Class-specific producer’s accuracies (PA), user’s accuracies (UA), and overall accuracies (OA) (%) for the different classifiers (Manas, training sample number = 4,000).

图像分类

分类器性能评估

Class-specific producer’s accuracies (PA), user’s accuracies (UA), and overall accuracies (OA) (%) for the different classifiers (Manas, training sample number = 4,000).

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

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

MOESM1 of Improved metagenomic analysis with Kraken 2

元基因组分析

分类器性能评估

Additional file 1: Table S1. Comparison of accuracy and computational performance. Table S2. Comparison of Kraken 2 with other classifiers, using various parameter values. Table S3. Genomes excluded i

DataCite Commons2020-08-26 更新50

Performance of the classification models before applying dual-GAN.

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