## 5 In the Search for a Missing Link
### Outcome Predictions
#### 5.3.5 Scenario #5: Selected Number of Chasers and Time Advantage  
In the next scenario, we used the selected number of chasers and the chasers' time in the selected offer to predict the game's outcome. In this case, none of the input features depended on the contestant's profile. By doing so, we wanted to examine the model's ability to predict the game's outcome based solely on objective, game-specific data. As presented in Table 16, several models reached high performances. The SVM, GNB classifier, and voting classifier performed better when trained only on the original data. The rest of the models were more successful when trained in the oversampled dataset. Five models had identical accuracies, precisions, recalls, and F1-scores of 77.1%, 82.9%, 77.1%, and 72.2%, respectively. As expected, the models failed to correctly predict the \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Method** & **SMOTE** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline Logistic Regression & + & 0.65714 & 0.65714 & 0.65714 & 0.65714 \\ K-Nearest Neighbors & + & 0.71429 & 0.69388 & 0.71429 & 0.69353 \\ Gaussian Naive Bayes & - & 0.71429 & 0.70238 & 0.71429 & 0.65306 \\
**Neural Network** & + & **0.80769** & **0.90385** & **0.80769** & **0.82675** \\ Decision Tree & + & 0.71429 & 0.69143 & 0.71429 & 0.67659 \\ Voting Classifier & + & 0.71429 & 0.70208 & 0.71429 & 0.70571 \\ Random Forest & + & 0.71429 & 0.69388 & 0.71429 & 0.69353 \\ XGBoost & + & 0.71429 & 0.69388 & 0.71429 & 0.69353 \\ \hline \hline \end{tabular}
\end{table} 
Table 15: Outcome predictions based on the selected number of chasers and the cash builder result. game's outcome in cases when a contestant managed to unexpectedly defeat all five chasers despite a small time advantage they had over the chasers. Several model combinations were tested for a joint voting classifier (_soft_ voting). The best-performing combination was using (1) an SVM (\(C=10\), \(gamma=``scale\"\)), (2) XGBoost (42 estimators, \(lr=0.05\)), and (3) a kNN model (\(k=5\), \(p=2\)). For both the SVM and XGBoost, we used the same parameters as those for when these models were used as a part of the voting classifier. The only model that managed to handle a part of the outliers and achieve performance metrics over 80% was the neural network. With its network architecture unchanged from those of the previous scenarios and a change in the batch size to 32, it reached the optimum performance after 105 epochs. The confusion matrices for the pruned DT (visualized later in the text) and the best-performing model in this scenario are shown in Figure 9. The total number of test samples differs between the models because for the pruned DT, we used 20% of the dataset for testing, while for the neural network, 15% of the dataset was allocated for validation, leaving only 15% for testing. As seen in the left-hand-side confusion matrix, the pruned DT accurately recognized the contestants that lost the game but struggled to identify the victorious ones, often misclassifying them. On the right-hand side, it is visible that the neural network was more successful in recognizing the contestants who would win the game. In Figure 10, we present the final pruned DT (\(ccp\_alpha=0.004\)) for this scenario. Although the pruned DT achieved a performance equal to those of the SVM, GNB classifier, voting classifier, and XGBoost, it stands out as the most interpretable model and the easiest to visualize among them. Its transparent tree structure clearly shows which conditions led to each decision of the model.