Research Article
Impact of Parameter Tuning for Optimizing Deep Neural Network Models for Predicting Software Faults
Table 3
Comparative results for the KC3 dataset.
| KC3 | Algorithm | Precision | Recall | F-measure | Accuracy |
| RF | 0.832 | 0.968 | 0.895 | 81.440 | DT | 0.870 | 0.930 | 0.899 | 82.990 | NB | 0.863 | 0.880 | 0.971 | 78.870 | Without dropout DNN | 0.890 | 0.870 | 0.880 | 93.000 | With dropout DNN | 0.910 | 1.000 | 0.980 | 97.000 |
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