## 3 Results
### Optimized Hyper-Parameter
\begin{table}
\begin{tabular}{c c c c c} \hline \multicolumn{2}{c}{**Model**} & \multicolumn{3}{c}{**Optimized Parameters**} \\ \hline
**Algorithm** & **Imputation** & **Grid Search** & **Random Search** & **Bayesian Search** \\ \hline \multirow{8}{*}{Mean} & \multirow{4}{*}{Mean} & bootstrap = True; max & bootstrap = False; max & bootstrap = False; max \\  & & depth = 20; max & depth = 20; max features & depth = 20; max features \\  & & feature = 3; min samples & = 3; min samples & = 3; min samples \\  & & leaf = 3; min samples & leaf = 3; min samples & leaf = 4; min samples \\  & & split = 5; & split = 5; & split = 5; \\  & & n\_estimators = 300 & n\_estimators = 200 & n\_estimators = 300 \\  & & bootstrap = True; max & bootstrap = True; max & bootstrap = False; max \\  & & depth = 20; max feature & depth = None; max & depth = 20; max \\  & & = 3; min samples & features = 3; min & features = 3; min \\  & & leaf = 4; min samples & samples leaf = 3; min & samples leaf = 3; min \\  & & split = 10; & samples split = 10; & samples split = 2; \\ RF & & n\_estimators = 300 & n\_estimators = 300 & n\_estimators = 200 \\  & & bootstrap = True; max & bootstrap = False; max & bootstrap = False; max \\  & & depth = 20; max & depth = 20; max features & depth = 20; max features \\  & & kNN & feature = 2; min samples & = 3; min samples & = 3; min samples \\  & & leaf = 3; min samples & leaf = 4; min samples & leaf = 4; min samples \\  & & split = 10; & split = 10; & split = 2; \\  & & n\_estimators = 300 & n\_estimators = 500 & n\_estimators = 300 \\  & & bootstrap = True; max & bootstrap = False; max & bootstrap = False; max \\  & & depth = 20; max & depth = None; max & depth = 30; max \\  & & feature = 2; min samples & feature = 2; min samples & feature = 3; min samples \\  & & leaf = 3; min samples & leaf = 3; min samples & leaf = 5; min samples \\  & & split = 5; & split = 5; & split = 10; \\  & & n\_estimators = 300 & n\_estimators = 200 & n\_estimators = 300 \\ \hline \multirow{8}{*}{XGBoost} & \multirow{4}{*}{Mean} & learning rate = 0.2; max & learning rate = 0.01; max & learning rate = 0.01; \\  & & depth = 7; & depth = 10; & max depth = 7; \\  & & subsample = 1; & subsample = 0.7; & subsample = 0.7; \\  & & colsample\_bytes = 1; & colsample\_bytes = 0.7; & colsample\_bytes = 0.5; \\  & & n\_estimators = 100 & n\_estimators = 300 & n\_estimators = 150 \\  & & learning rate = 0.15; max & learning rate = 0.01; & learning rate = 0.01; max \\  & & depth = 7; & max depth = 10; & depth = 10; \\  & & subsample = 0.5; & subsample = 0.7; & subsample = 0.7; \\  & & subsample = 1; & colsample\_bytes = 1; & colsample\_bytes = 0.5; \\  & & colsample\_bytes = 1; & colsample\_bytes = 1; & colsample\_bytes = 1; \\  & & n\_estimators = 100 & n\_estimators = 300 & n\_estimators = 300 \\  & & learning rate = 0.1; max & learning rate = 0.01; max & learning rate = 0.1; max \\  & & depth = 7; & max depth = 10; & depth = 10; \\  & & subsample = 0.5; & subsample = 0.5; & subsample = 0.5; \\  & & colsample\_bytes = 1; & colsample\_bytes = 1; & colsample\_bytes = 1; \\  & n\_estimators = 100 & n\_estimators = 300 & n\_estimators = 300 \\  & & learning rate = 0.1; max & learning rate = 0.01; max & learning rate = 0.1; max \\  & & depth = 5; & depth = 7; & depth = 5; \\  & & subsample = 1; & subsample = 0.5; & subsample = 0.7; \\  & & colsample\_bytes = 1; & colsample\_bytes = 0.7; & colsample\_bytes = 0.7; \\  & & n\_estimators = 100 & n\_estimators = 200 & n\_estimators = 50 \\ \hline \end{tabular}
\end{table} 
Table 2: _Cont._without an external CPU. Although the processing time would differ from other computer software and hardware, our comparison provides valuable insights into hyper-parameter optimization on machine learning-based models. Table 3 shows the average processing time of each model according to the optimization method. We found that Grid Search optimization consumed more processing time than other optimization methods, while Bayesian Search provided the fastest processing time.