Research Article
Impact of Parameter Tuning for Optimizing Deep Neural Network Models for Predicting Software Faults
Table 2
Comparative results for the KC1 dataset.
| KC1 | Algorithm | Precision | Recall | F-measure | Accuracy |
| RF | 0.887 | 0.965 | 0.925 | 86.670 | DT | 0.865 | 0.974 | 0.916 | 84.870 | NB | 0.888 | 0.905 | 0.897 | 82.360 | Without dropout DNN | 0.790 | 0.970 | 0.940 | 88.570 | With dropout DNN | 0.850 | 1.000 | 0.920 | 92.000 |
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