Open Access

Table 2

Classifiers considered for ML-based comparisons according to selected parameters.

ID Type # Estimator Max. depth # Layers Units per layer AUC single pop. AUC Transformer
LC Linear 0.57 0.54
SVC Support Vector Classifier 0.55 0.54
RF1 Random Forrest 10 5 0.57 0.55
RF2 Random Forrest 10 20 0.53 0.55
RF3 Random Forrest 50 5 0.55 0.54
RF4 Random Forrest 50 20 0.53 0.57
DNN1 Deep Neural Network 1 16 0.58 0.57
DNN2 Deep Neural Network 1 32 0.59 0.58
DNN3 Deep Neural Network 1 64 0.59 0.52
DNN4 Deep Neural Network 1 128 0.60 0.56
DNN5 Deep Neural Network 3 16 0.60 0.57
DNN6 Deep Neural Network 3 32 0.62 0.58
DNN7 Deep Neural Network 3 64 0.59 0.58
DNN8 Deep Neural Network 3 128 0.58 0.57
DNN9 Deep Neural Network 5 16 0.60 0.58
DNN10 Deep Neural Network 5 32 0.58 0.56
DNN11 Deep Neural Network 5 64 0.59 0.59
DNN12 Deep Neural Network 5 128 0.59 0.57

Notes. The AUC is obtained when classifying two similar parts of the same population (see text for details). All classifiers were computed using the Sci-kit library (see https://scikit-learn.org/stable/) or (for deep neural networks – DNN) the Keras library (see https://keras.io). The classifier, AUC Single pop., refers to the AUC when classifying the numerical population only (with random labels), while AUC Transformer refers to the AUC for the last iteration of the generative model (baseline model).

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