## 4 Discussion  
The APS dataset was used as an empirical study on various topics related to classic machine learning; we reviewed some of those published between 2016 and 2019 March. [PERSON] et al. [10] applied mean imputation and the Soft-Impute algorithm to handle missing data and concluded that RF was the best classifier owing to the highest cost-wise ratio of 92.56%, where the FP and FN rates were 3.74% and 3.70%, respectively. [PERSON] et al. [11] removed attributes with over 50% missing data, conducted metafeature engineering to generate new attributes, implemented Synthetic Minority Oversampling Technique (SMOTE) to replace the removed examples, and then concluded that XGBoost with meta-features yielded the lowest average cost and deviance. [PERSON] et al. [12] applied median imputation address missing data and concluded that RF can be used as a cost function providing better results than the naive approaches of checking every truck or no truck until failure. [PERSON] et al. [13] introduced an optimized k-NN approach to handle missing values and created a tailored k-NN model using a specified HEIM distance. [PERSON] and [PERSON] [14] removed attributes with more than 10% missing values and applied mean imputation to the remaining attributes. They evaluated various classifiers, including RF, SVM, and k-NN, and selected RF, which returned an accuracy score of 0.99. [PERSON] et al. [15] used five imputation techniques, including expectation-maximization, mean imputation, Soft-Impute, Multivariate Imputation by Chained Equation (MICE), and iterative singular value decomposition, and applied five classifiers, including naive Bayes (NB), k-NN, SVM, \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Training Set (for Modeling)**} & \multicolumn{4}{c}{**Test Set (for Validation)**} \\ \hline
**Group** & \(\beta\) & \(\beta_{opt}\) & \(\beta_{opt,payoff}\) & \(X_{c}\) & \(\beta\) & \(\beta_{opt}\) & \(\beta_{opt,payoff}\) & \(X_{c}\) \\ \hline CTRL & 0.0038 & 0.0042 & 0.0024 & \(-\)1.7101 & 0.0049 & 0.0060 & 0.0019 & \(-\)1.6587 \\ EXP1 & 0.0003 & 0.0018 & 0.0004 & \(-\)2.1413 & 0.0026 & 0.0031 & 0.0021 & \(-\)1.7731 \\ EXP2 & 0.0011 & 0.0032 & 0.0009 & \(-\)1.9241 & 0.0013 & 0.0044 & 0.0007 & \(-\)1.8992 \\ EXP3 & 0.0152 & 0.0071 & 0.0056 & \(-\)1.4396 & 0.0142 & 0.0093 & 0.0039 & \(-\)1.4646 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Summary of \(\beta\) and \(X_{c}\) values for the selected LR model, for control and experimental groups. Figure 3: Decile-wise lift chart for the logistic regression (LR) and k-nearest neighbor (k-NN) models: (**a**) is for NEG class label, (**b**) is for POS class label. RF, and GBT. They concluded that NB performs better on the actual and imbalanced dataset; however, RF performed better on the balanced dataset after applying an under-sampling method. In addition, the mean imputation was identified as the best method for imputing missing values. However, if the primary concern is the FP rate rather than accuracy, then Soft-Impute was shown to outperform the other imputation techniques and NB was demonstrated to have the best performance compared to the other classifiers. [PERSON] and [PERSON] [16] employed a k-NN algorithm for missing data imputation, implemented an improved RF algorithm to reduce both the FP and FN misclassification rates, and demonstrated competitive results in terms of precision, F-measure, and MCC. Table 7 compares the results achieved in this paper for the EXP2-LR combination with those of the previous studies in terms of accuracy, F-measure, and MCC. The previous studies included for the comparison applied imputation techniques directly to missing data in the training set; however, the process is not discussed and assumptions related to the consistency of the training and test datasets were not provided. In addition, the training and test sets were not evaluated after applying imputation to determine if they satisfy the assumption of a consistent distribution.