Table 16 Comparison of multiple classes to predict loyalty
Algorithm | Accuracy | Precision | Recall | F1-score | Weighted |
|---|---|---|---|---|---|
MLPC Confusion matrix 1246.0 0.0 0.0 0.0 0.0 97.0 0.0 0.0 0.0 0.0 616.0 0.0 0.0 0.0 0.0 334.0 0.0 0.0 0.0 0.0 17.0 0.0 0.0 0.0 0.0 | 0.55 | Precision (1.0)Â = 0.53 Precision (2.0) = 0.0 Precision (3.0) = 0.0 Precision (4.0) = 0.0 Precision (5.0) = 0.50 | Recall (1.0) = 1.0 Recall (2.0) = 0.0 Recall (3.0) = 0.0 Recall (4.0) = 0.0 Recall (4.0) = 0.50 | F1-score (1.0)Â = 0.70 F1-score (2.0)Â = 0.0 F1-score (3.0)Â = 0.0 F1-score (4.0) = 0.0 F1-score (5.0)Â = 0.0 | Weighted precision: 0.26 Weighted recall: 0.51 WeightedF1 score: 0.34 Weighted false positive rate: 0.51 |
DTC Confusion matrix 995.0 8.0 130.0 66.0 0.0 74.0 13.0 4.0 2.0 0.0 211.0 7.0 297.0 97.0 0.0 54.0 0.0 70.0 211.0 0.0 8.0 0.0 2.0 2.0 0.0 | 0.67 | Precision (1.0) = 0.74 Precision (2.0) = 0.46 Precision (3.0) = 0.59 Precision (4.0) = 0.55 Precision (5.0) = 0.59 | Recall (1.0) = 0.82 Recall (2.0) = 0.13 Recall (3.0) = 0.48 Recall (4.0) = 0.62 Recall (5.0) = 0.60 | F1-score (1.0) = 0.78 F1-score (2.0) = 0.21 F1-score (3.0) = 0.53 F1-score (4.0) = 0.59 F1-score (5.0) = 0.60 | Precision: 0.65 Recall: 0.53 F1 score: 0.65 False positive rate:0.22 |
RFC Confusion matrix 1073.0 2.0 80.0 44.0 0.0 73.0 12.0 7.0 1.0 0.0 185.0 3.0 341.0 83.0 0.0 40.0 0.0 46.0 249.0 0.0 1.0 0.0 6.0 5.0 0.0 | 0.74 | Precision (1.0) = 0.74 Precision (2.0) = 0.70 Precision (3.0) = 0.71 Precision (4.0) = 0.65 Precision (5.0) = 0.74 | Recall (1.0) = 0.89 Recall (2.0) = 0.12 Recall (3.0) = 0.55 Recall (4.0) = 0.74 Recall (5.0) = 0.74 | F1-score (1.0) = 0.83 F1-score (2.0) = 0.21 F1-score (3.0) = 0.62 F1-score (4.0) = 0.69 F1-score (5.0) = 0.66 | Precision: 0.73 Recall: 0.74 F1 score: 0.72 False positive rate:0.18 |