# An Improved Confounding Effect Model for Software Defect Prediction
## 4 Experiments and Results
### Experiments for the Proposed Prediction Model
#### 4.3.3. Presents of Prediction Result  
This section shows the prediction results of our proposed model and the baseline models. LR, CM-LR, SVM, and NN represent the baseline models, and HSR-LR represents the proposed model. Case 1: CM1  
CM1 contains 344 instances with a defect rate of 12%. The prediction results of different models on CM1 are shown in Table 5. Our proposed model outperformed the baseline models under the F1 score. Case 2: JM1  
JM1 contains 9593 instances with a defect rate of 18%. The prediction results of different models on JM1 are shown in Table 6. Our proposed model outperformed the baseline models under the F1 score. Figure 5: Heatmap for JM1 for different d and n values in strategy 2. \begin{table}
\begin{tabular}{c c c c} \hline
**Model** & **Precision** & **Recall** & **F1** \\ \hline LR & 50.0\% & 23.1\% & 0.316 \\ CM-LR & 50.0\% & 23.1\% & 0.316 \\ SVM & 31.0\% & 62.7\% & 0.415 \\ NN & 29.6\% & 65.8\% & 0.408 \\ HSR-LR & 40.0\% & 46.2\% & 0.429 \({}^{*}\) \\ \hline \end{tabular} The \({}^{*}\) indicates the best performance of the models.  
\end{table} 
Table 5: Results on CM1. +++ ==ERROR: No output for this page==
Case 3: KC1  
KC1 contains 2096 instances with a defect rate of 16%. The prediction results of different models on KC1 are shown in Table 7. Our proposed model outperformed the baseline models under the F1 score. Case 4: MC2  
MC2 contains 127 instances with a defect rate of 35%. The prediction results of different models on MC2 are shown in Table 8. Our proposed model outperformed the baseline models under the F1 score. Case 5: PC3  
PC3 contains 1125 instances with a defect rate of 12%. The prediction results of different models on PC3 are shown in Table 9. Our proposed model outperformed the baseline models in the F1 score. Case 6: PC3  
PC3 contains 1125 instances with a defect rate of 12%. The prediction results of different models on PC3 are shown in Table 9. Our proposed model outperformed the baseline models in the F1 score. Case 7: PC3  
PC3 contains 1125 instances with a defect rate of 12%. The prediction results of different models on PC3 are shown in Table 9. Our proposed model outperformed the baseline models in the F1 score. Case 8: PC3  
PC3 contains 1125 instances with a defect rate of 12%. The prediction results of different models on PC3 are shown in Table 9. Our proposed model outperformed the baseline models in the F1 score. Case 9: PC3  
PC3 contains 1125 instances with a defect rate of 12%. The prediction results of different models on PC3 are shown in Table 9. Our proposed model outperformed the baseline models in the F1 score. Case 10: PC3  
Case 11: PC3 contains 1125 instances with a defect rate of 12%. The prediction results of different models on PC3 are shown in Table 9. Our proposed model outperformed the baseline models in the F1 score. Case 12: PC3 contains 1125 instances with a defect rate of 12%. The prediction results of different models on PC3 are shown in Table 9. Our proposed model outperformed the baseline models in the F1 score. Case 13: PC3 contains 1125 instances with a defect rate of 12%. The prediction results of different models on PC3 are shown in Table 7.0 Our proposed model outperformed the baseline models in the F1 score. Case 14: PC3 contains 1125 instances with a defect rate of 12%. The prediction results of different models on PC3 are shown in Table 7.1 Our proposed model outperformed the baseline models in the F1 score. Case 15: PC3  
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Case 6: PC5  
PC5 contains 17,001 instances with a defect rate of 3%. The prediction results of different models on PC5 are shown in Table 7.2 Our proposed model outperformed the baseline models in the F1 score. The above results present the performance of our proposed model and baseline models. We have the following observations and analysis:  
1. Compared with all baseline models, our proposed HSR-LR model performs best under the F1 score, and it does not obtain the best performance in precision and recall rate.