Table 4 Comparison of evaluation metrics...

Table 4

Comparison of evaluation metrics by model

Model	Accuracy	Precision	Recall	F1 score
XGBoost	0.913	0.767	0.711	0.738
LightGBM	0.913	0.774	0.695	0.733
RF	0.912	0.783	0.673	0.724
RNN	0.898	0.733	0.661	0.695
DT	0.896	0.713	0.686	0.699
LSTM	0.896	0.733	0.645	0.686
AdaBoost	0.895	0.732	0.642	0.684
CNN	0.894	0.721	0.650	0.684
SVM	0.891	0.782	0.530	0.632
LR	0.880	0.766	0.464	0.578
MLP	0.879	0.669	0.623	0.645
LDA	0.875	0.741	0.454	0.563
DNN	0.862	0.625	0.552	0.586
KNN	0.857	0.857	0.230	0.362

Model	Accuracy	Precision	Recall	F1 score
XGBoost	0.913	0.767	0.711	0.738
LightGBM	0.913	0.774	0.695	0.733
RF	0.912	0.783	0.673	0.724
RNN	0.898	0.733	0.661	0.695
DT	0.896	0.713	0.686	0.699
LSTM	0.896	0.733	0.645	0.686
AdaBoost	0.895	0.732	0.642	0.684
CNN	0.894	0.721	0.650	0.684
SVM	0.891	0.782	0.530	0.632
LR	0.880	0.766	0.464	0.578
MLP	0.879	0.669	0.623	0.645
LDA	0.875	0.741	0.454	0.563
DNN	0.862	0.625	0.552	0.586
KNN	0.857	0.857	0.230	0.362

Note(s): This table presents the comparison of evaluation metrics by 14 AI models. Detailed explanations of Accuracy, Precision, Recall, and F1 Score are described on Table 3

Source(s): Table by authors

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