. Author manuscript; available in PMC: 2023 Jan 1.

Published in final edited form as: Comput Speech Lang. 2021 Jul 19;71:101263. doi: 10.1016/j.csl.2021.101263

Table 4:

Disclosure task performance using session-level aggregated features. Using feature significance of p <0.20.

Model	F1	Acc.	Prec.	FNR	Pos. Acc.	Neg. Acc.
DT	0.452	0.641	0.451	0.259	0.471	0.725
RF	0.229	0.633	0.327	0.316	0.186	0.849
GNB	0.480^*	0.603	0.434	0.256	0.557	0.622
L-SVM	0.609 ^*	0.703 ^*	0.531 ^*	0.161 ^*	0.719	0.697

indicates performance better than randomized bootstrap σ_d. Bold values indicate the best performance in their respective columns. Pos. Acc. and Neg. Acc. indicate the accuracy for the positive and negative classes, respectively. The best performing L-SVM hyperparameters, shown in the table above, are C = 0.1 along with balanced class weights. The decision tree classifier used entropy as the splitting criteria, while the random forest classifier used Gini impurity.