# Data-Driven Decision Support in SaaS Cloud-Based Service Models
## 4 Data-Driven Decision Support in SaaS
### Machine Learning Methods
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Ref.**} & **Proposed** & **Compared** & \multicolumn{3}{c}{**Evaluation Results**} & \multirow{2}{*}{**Validation**} & \multirow{2}{*}{**Dataset**} \\  & **Method** & **Methods** & **Common Metrics** & & **Metrics** & **Method** & **Dataset** \\ \hline \multirow{11}{*}{**[14]**} & & & \multicolumn{3}{c}{Random forest:} & \multirow{9}{*}{**[19]**} & \multirow{10}{*}{**[17]**} \\  & & & 0.88 Training Accuracy, & & & \\  & & & 0.87 Test Accuracy & & & \\  & & Decision & Decision Trees: 1.00 & & & \\  & & Trees, & (overfitting) Training & & & A B2B SaaS \\  & & Support & Accuracy, 0.76 Test Accuracy & & & \\  & & Support & Support Vector Machine: & & & \\  & Vector & 1.00 (overfitting) Training & & train-test split & not mentioned) \\  & Random & Machine, & Accuracy, 0.63 Test Accuracy & - & (percentage not & including \\  & Forest & Neural Networks: & & mentioned) & 7044 examples of \\  & & Naive Bayes, & 0.85 Training Accuracy, & & B2B SaaS \\  & Logistic & & 0.82 Test Accuracy & & & \\  & Regression & Naive Bayes: 0.71 Training & & & 21 variables \\  & & Regression & Accuracy, 0.69 Test Accuracy & & & \\  & & Logistic Regression: & & & \\  & & & 0.73 Training Accuracy, & & & \\  & & & 0.71 Test Accuracy & & & \\ \hline \multirow{11}{*}{**[22]**} & & & XGBoost: 0.948 Accuracy, & & & \\  & & & 0.9545 Precision, & & & \\  & & & 0.9418 Recall, & & & \\  & & Transformer- & 0.9841 F1-Score, 0.988 AUC & & & \\  & based models & 0.8679 Accuracy, & & train-test split & & \\  & (FT- & 0.8477 Precision, & - & (percentage not & game includes data \\  & transformer), & 0.8875 Recall, & & mentioned) & from over \\  & GBDT & 0.8671 F1-Score, 0.949 AUC & & & 268,370 users \\  & & & GBDT: 0.8561 Accuracy, & & & \\  & & & 0.8755 Precision, & & \\  & & & 0.8328 Recall, & & & \\  & & & 0.8536 F1-Score, 0.934 AUC & & & \\ \hline \multirow{11}{*}{**[19]**} & & & Hybrid Model: & & & \\  & & & 0.9567 Accuracy, & & & \\  & & & 0.943 Precision, & & & \\  & & & 0.9565 Recall, 0.943 F1-Score & & & \\  & & ANN: 0.789 Accuracy, & & & \\  & & & 0.8403 Precision, & & \\  & & & 0.8824 Recall, & & & \\  & & & 0.8608 F1-Score & & & \\ Hybrid & KNN, & Random Forest: & & & \\  & Random & 0.8 Accuracy, 0.79 Precision, & & & \\  & Forest, ANN, & 0.80 Recall, 0.79 F1-Score & & train-test split & & \\ Naive Bayes & Decision tree & KNN: 0.839 Accuracy, & & & \\  & & 0.826 Precision, 0.829 Recall, & & & \\  & & & 0.781 F1-Score & & & \\  & & Decision Tree: & & & \\  & & & 0.9097 Accuracy, & & \\  & & & 0.9242 Precision, & & \\  & & & 0.9242 Recall, & & \\  & & & 0.9242 F1-Score & & \\  & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._  
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\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Ref. **} & \multirow{2}{*}{**Proposed**} & \multirow{2}{*}{**Compared**} & \multicolumn{2}{c}{**Evaluation Results**} & \multirow{2}{*}{**Validation**} & \multirow{2}{*}{**Dataset**} \\  & & & & & & \\ \cline{5-6} \cline{8-8}  & \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Methods**} & & \multicolumn{1}{c}{**Common Metrics**} & & & \\ \hline \multicolumn{8}{c}{Gradient Boosting Machine:} \\ \multicolumn{8}{c}{0.7935 Accuracy,} \\ \multicolumn{8}{c}{0.7946 Precision,} \\ \multicolumn{8}{c}{0.8402 Recall,} \\ \multicolumn{8}{c}{0.8167 F1-Score,} \\ \multicolumn{8}{c}{0.86 ROC AUC} \\ \hline \multicolumn{8}{c}{Random Forest:} \\ \multicolumn{8}{c}{Forest} \\ \multicolumn{8}{c}{0.88 Training Accuracy,} \\ \multicolumn{8}{c}{0.87 Test Accuracy} \\ \multicolumn{8}{c}{0.73 Training Accuracy,} \\ \multicolumn{8}{c}{Naïve Bayes} \\ \multicolumn{8}{c}{1.00 (overfitting) Training} \\ \multicolumn{8}{c}{1.