我使用Delonge方法比较两个ROC AUC。它的结果是z得分。
从 sklearn 软件包获得的LDA（线性判别分析）获得的两个ROC AUC。第一个使用 eigen LDA内的求解器，第二个使用 svd 求解器。
虚线是我的数据。 The red line is N(0, 1)
Note: there is a minor jump at the point Z = 0.
Z = 0 means that classifiers did their job equally.
z＆gt; 0（z＆lt; 0）意味着第一个（第二）分类器的工作更好。
Corresponding histogram:
This graphic shows the resuls of classification of 1 iteration (it won't be noticeable when compare 2 classifiers with this kind of plot so I just insert one). 正常观察值的量是贫血观测值的 4倍。 贫血观察值的方差为 10倍的正常观察值。
The question in the title.我如何指出某些事实和/或原因，以解释我的z得分数据的行为（z = 0点中分离的正常混合物）？
I use DeLonge method to compare two ROC AUCS. The result of it is Z-score.
Both ROC AUCs obtained from LDA (linear discriminant analysis) from sklearn package. The first one uses eigen solver inside LDA and the second one uses svd solver.
The dotted line is my data. The red line is N(0, 1)
Note: there is a minor jump at the point Z = 0.
Z = 0 means that classifiers did their job equally.
Z > 0 (Z < 0) means that the first (second) classifier did its job better.
Corresponding histogram:
This graphic shows the resuls of classification of 1 iteration (it won't be noticeable when compare 2 classifiers with this kind of plot so I just insert one). The amount of normal observations are 4 times of the amount of anemia observations. The variance of anemia observations are 10 times of the variance of normal observations.
The question in the title. How can I point at some facts and/or reasons that will explain the behaviour of my Z-score data (Normal mixture with separation in point Z = 0)?
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