When the model allows changing species prevalence (i.e. GAM, GLM, BRT, CART), a prevalence of 50% was used in model weights. Performance is shown in terms of classification rate (AUC, sensitivity and

Figshare2015-12-02 更新10

Proportional odds simulation.

比例优势模型

分类性能评估

Percentiles and biases of cutpoints selected from cumulative ROC curves with several criteria and computed parametrically: 10,000 simulated datasets parameterized for proportional odds and AUC1 = AUC2

NIAID Data Ecosystem40

Comparison of classification performance between the method presented in this paper and the classification approach of Burgos-Artizzu et al. [23].

小鼠行为

分类性能评估

The results are the average across all the mouse pairs of Dataset A, computed employing all the metrics of Table 2 and 3. In all instances our approach outperforms [23]. The full results are available

NIAID Data Ecosystem30

<p>Performance metrics of classifiers with count vectorizer.</p>

资源简介：

相关数据集