Table 3 Comparative CV performance... | Emerald Publishing

Table 3

Comparative CV performance results between RF and NN models for apartments

Apartments models
Evaluation metric	Random forest			Neural network
Evaluation metric	OS	CWB	IMB	OS	CWB	IMB
CV score	0.89 ± 0.02	0.88 ± 0.02	0.90 ± 0.02	0.83 ± 0.02	0.83 ± 0.03	0.87 ± 0.02
Recall – 0	0.52 ± 0.16	0.78 ± 0.15	0.47 ± 0.12	0.65 ± 0.19	0.70 ± 0.20	0.00 ± 0.00
Recall – 1	0.68 ± 0.11	0.76 ± 0.08	0.52 ± 0.12	0.68 ± 0.08	0.75 ± 0.09	0.44 ± 0.11
Recall – 2	0.94 ± 0.02	0.90 ± 0.01	0.96 ± 0.02	0.85 ± 0.02	0.85 ± 0.02	0.96 ± 0.02
Precision - M	0.73 ± 0.08	0.71 ± 0.06	0.77 ± 0.07	0.59 ± 0.07	0.59 ± 0.03	0.47 ± 0.02
Precision - 0	0.64 ± 0.21	0.65 ± 0.17	0.77 ± 0.21	0.41 ± 0.07	0.35 ± 0.09	0.00 ± 0.00
Precision - 1	0.59 ± 0.12	0.50 ± 0.07	0.61 ± 0.12	0.40 ± 0.06	0.43 ± 0.06	0.49 ± 0.12
Precision - 2	0.96 ± 0.01	0.98 ± 0.01	0.94 ± 0.02	0.97 ± 0.02	0.98 ± 0.01	0.92 ± 0.01
F1 score- M	0.70 ± 0.05	0.74 ± 0.05	0.68 ± 0.05	0.62 ± 0.06	0.63 ± 0.05	0.46 ± 0.04
F1 score- 0	0.52 ± 0.12	0.68 ± 0.14	0.56 ± 0.14	0.47 ± 0.14	0.44 ± 0.10	0.00 ± 0.00
F1 score- 1	0.62 ± 0.10	0.60 ± 0.07	0.54 ± 0.09	0.50 ± 0.06	0.54 ± 0.06	0.45 ± 0.11
F1 score- 2	0.95 ± 0.01	0.93 ± 0.01	0.95 ± 0.01	0.91 ± 0.01	0.91 ± 0.01	0.94 ± 0.01
Roc AUC - M	0.89 ± 0.03	0.90 ± 0.03	0.88 ± 0.03	0.89 ± 0.05	0.88 ± 0.04	0.80 ± 0.02
Roc AUC - 0	0.95 ± 0.04	0.95 ± 0.03	0.97 ± 0.02	0.94 ± 0.06	0.93 ± 0.06	0.93 ± 0.04
Roc AUC - 1	0.92 ± 0.03	0.91 ± 0.03	0.92 ± 0.03	0.88 ± 0.04	0.88 ± 0.04	0.91 ± 0.04
Roc AUC - 2	0.95 ± 0.02	0.95 ± 0.02	0.95 ± 0.02	0.94 ± 0.03	0.94 ± 0.02	0.94 ± 0.03

Apartments models
Evaluation metric	Random forest			Neural network
Evaluation metric	OS	CWB	IMB	OS	CWB	IMB
CV score	0.89 ± 0.02	0.88 ± 0.02	0.90 ± 0.02	0.83 ± 0.02	0.83 ± 0.03	0.87 ± 0.02
Recall – 0	0.52 ± 0.16	0.78 ± 0.15	0.47 ± 0.12	0.65 ± 0.19	0.70 ± 0.20	0.00 ± 0.00
Recall – 1	0.68 ± 0.11	0.76 ± 0.08	0.52 ± 0.12	0.68 ± 0.08	0.75 ± 0.09	0.44 ± 0.11
Recall – 2	0.94 ± 0.02	0.90 ± 0.01	0.96 ± 0.02	0.85 ± 0.02	0.85 ± 0.02	0.96 ± 0.02
Precision - M	0.73 ± 0.08	0.71 ± 0.06	0.77 ± 0.07	0.59 ± 0.07	0.59 ± 0.03	0.47 ± 0.02
Precision - 0	0.64 ± 0.21	0.65 ± 0.17	0.77 ± 0.21	0.41 ± 0.07	0.35 ± 0.09	0.00 ± 0.00
Precision - 1	0.59 ± 0.12	0.50 ± 0.07	0.61 ± 0.12	0.40 ± 0.06	0.43 ± 0.06	0.49 ± 0.12
Precision - 2	0.96 ± 0.01	0.98 ± 0.01	0.94 ± 0.02	0.97 ± 0.02	0.98 ± 0.01	0.92 ± 0.01
F1 score- M	0.70 ± 0.05	0.74 ± 0.05	0.68 ± 0.05	0.62 ± 0.06	0.63 ± 0.05	0.46 ± 0.04
F1 score- 0	0.52 ± 0.12	0.68 ± 0.14	0.56 ± 0.14	0.47 ± 0.14	0.44 ± 0.10	0.00 ± 0.00
F1 score- 1	0.62 ± 0.10	0.60 ± 0.07	0.54 ± 0.09	0.50 ± 0.06	0.54 ± 0.06	0.45 ± 0.11
F1 score- 2	0.95 ± 0.01	0.93 ± 0.01	0.95 ± 0.01	0.91 ± 0.01	0.91 ± 0.01	0.94 ± 0.01
Roc AUC - M	0.89 ± 0.03	0.90 ± 0.03	0.88 ± 0.03	0.89 ± 0.05	0.88 ± 0.04	0.80 ± 0.02
Roc AUC - 0	0.95 ± 0.04	0.95 ± 0.03	0.97 ± 0.02	0.94 ± 0.06	0.93 ± 0.06	0.93 ± 0.04
Roc AUC - 1	0.92 ± 0.03	0.91 ± 0.03	0.92 ± 0.03	0.88 ± 0.04	0.88 ± 0.04	0.91 ± 0.04
Roc AUC - 2	0.95 ± 0.02	0.95 ± 0.02	0.95 ± 0.02	0.94 ± 0.03	0.94 ± 0.02	0.94 ± 0.03

Note(s): Classes: 0 = Dissatisfied, 1 = Neutral, 2 = Satisfied, M = Macro

Source(s): Authors' own work