Predicting Young Imposter Syndrome Using Ensemble Learning

Table 2

Evaluation metrics for the random forest, neural network, and ensemble models.

	Random forest model	Neural network model	Ensemble model
Accuracy	0.935	0.963	0.964
Precision	1	1	1
Recall	1	1	1
F1	0.872727	0.933333	0.933334
Logloss	0.335263	0.263876	0.206847
AUC	0.971482	0.986442	0.988832
MCC	0.838383	0.905834	0.906743

For all models, accuracy, precision, recall, F1-score, logloss, AUC, and MCC values are measured as evaluation metrics.