## 5 In the Search for a Missing Link
### Outcome Predictions
#### 5.3.7 Discussion on Outcome Predictions  
By comparing all the analyzed cases, we noticed that the features that were not related to the contestant's profile, but instead to the game-specific details, had a greater impact on the game's outcome. Using only the information about the number of chasers and their assigned time, we managed to classify 84.6% of the test examples accurately. In five out of the six scenarios, the neural network stood out as the most successful predictor. The only scenario where the voting classifier outperformed the neural network was the last one, which predicted the game's outcome according to the information about the selected number of chasers, the cash builder result, and the chasers' time in the selected offer. Like in the previous analysis, where we considered the selected offer prediction, using too many  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Method** & **SMOTE** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline K-Nearest Neighbors & + & 0.77143 & 0.78561 & 0.77143 & 0.77598 \\ Gaussian Naive Bayes & - & 0.77143 & 0.82857 & 0.77143 & 0.72245 \\ Neural Network & + & 0.80769 & 0.85897 & 0.80769 & 0.82249 \\ Decision Tree & - & 0.74286 & 0.72976 & 0.74286 & 0.73012 \\ Pruned Decision Tree & + & 0.74286 & 0.72976 & 0.74286 & 0.73012 \\
**Voting Classifier** & + & **0.82857** & **0.82466** & **0.82857** & **0.82343** \\ Random Forest & + & 0.80000 & 0.80575 & 0.80000 & 0.78002 \\ XGBoost & - & 0.77143 & 0.77714 & 0.77143 & 0.74127 \\ \hline \hline \end{tabular}
\end{table}
Table 17: Outcome predictions based on the selected number of chasers, cash builder result, and chasers’ time in the selected offer.  
Figure 12: Features’ impacts on the neural network model’s output for the outcome prediction based on the selected number of chasers, cash builder result, and chasers’ time in the selected offer.  
features introduced noise to the models and decreased their successful prediction abilities. As in the previous subsection, we attempted to derive simple rules for outcome predictions using tree-based methods. However, the complexity of the trees and the small size of the test dataset hindered our ability to validate any rules reliably. Given the limited number of test samples and the large size of the tree, each terminal node contained only a low number of test cases, making it difficult to draw conclusions confidently.  
Given that the models used in the last three scenarios, which combine the selected number of chasers, the cash builder result, and the chasers' time in the selected offer, reached the best performance, we can consider these features as the most relevant ones. In most scenarios and models, their higher values pushed the model's output to predict the defeat of the contestant, while their lower values did the opposite. The only exception was the neural network, where only mid-range values for the cash builder result favored the prediction of the contestant's victory. Also, in scenario #5, a higher value for the chasers' time in the selected offer increased the neural network's output. The contestant's gender and whether the chasers recognized them were the most influential features of the contestant's profiles. Although the contestant's gender had different impacts across the different models, in the neural networks that had the best performance in both scenarios where the contestant's profile was taken into account, a higher value for the gender tended to push the model's output toward higher values. Not being recognized by the chasers also led the models toward predicting a positive outcome. A number of the methods treated the participation of Chaser #4 as one of the most important features, pushing the model's output toward predicting the negative output if that chaser was present.