Table 1.
Accuracy of different approaches to machine learning.
| Question | Overall rating | Cleanliness | Dignity and respect | |
| Naïve Bayes multinomial |
|
|
|
|
|
|
ROC | 0.94 | 0.88 | 0.91 |
|
|
F measure | 0.89 | 0.84 | 0.85 |
|
|
Accuracy (%) | 88.6 | 81.2 | 83.7 |
|
|
Time (s) | 0.11 | 0.05 | 0.06 |
| Decision trees |
|
|
|
|
|
|
ROC | 0.84 | 0.76 | 0.79 |
|
|
F measure | 0.81 | 0.86 | 0.8 |
|
|
Accuracy (%) | 80.8 | 88.4 | 83 |
|
|
Time (s) | 552 | 206 | 332 |
| Bagging |
|
|
|
|
|
|
ROC | 0.89 | 0.83 | 0.87 |
|
|
F measure | 0.82 | 0.87 | 0.85 |
|
|
Accuracy (%) | 82.5 | 89.2 | 84.5 |
|
|
Time (s) | 4871 | 2018 | 3164 |
| Support vector machine |
|
|
|
|
|
|
ROC | 0.79 | 0.53 | 0.6 |
|
|
F measure | 0.84 | 0.84 | 0.8 |
|
|
Accuracy (%) | 84.6 | 88.5 | 84.1 |
|
|
Time (s) | 612 | 305 | 520 |