## 5 In the Search for a Missing Link
### Outcome Predictions
#### 5.3.2 Scenario #2: Contestant's Profile, Cash Builder Result, and Offers  
In Table 13, we report the outcome prediction results based on additional data. In this case, the models also took into account the information about the received offers, the cash builder result, and the selected offer, including the chasers who participated in the second phase of the game. We can notice that in this scenario, most of the models performed better when trained on oversampled datasets. The only method that performed better without SMOTE was the kNN model (\(k=1\), \(p=1\)). Adding information about the specific offers improved the performances of all the methods, regardless of their complexity. Three completely different models achieved all the performance metrics above 70%. Although the voting classifier outperformed the neural network, in this scenario, in terms of the accuracy, precision, and recall, we reported the neural network as the most successful model, as we opted to grade in accordance with the F1-score. Its architecture remained the same, but the model that generalized the best was obtained after only 19 epochs. The voting classifier (_hard_ voting) consisted of three models: (1) a kNN model (\(k=1\), \(p=2\)), (2) XGBoost (24 estimators, \(lr=1.0\)), and (3) an SVM (\(C=1\), \(gamma=1\)). The parameters for the SVM and XGBoost models were the same as those for when these models were a part of the voting classifier.  
Regarding the features' importance, the SVM and kNN models considered the participation of specific chasers in the selected offer as some of the most relevant features, especially Chasers #4 and #5. The contestant's gender also played a significant role in the SVM model, while the field of work stood out as an important feature in the kNN model. In both models, the cash builder result and whether the chasers recognized the contestant were also important features. In the tested tree-based methods (pruned DT with \(ccp\_alpha=0.019\), RF with 50 estimators, and XGBoost), the features of the selected offer, such as the time and prize money, were the most important. The best-performing model (the neural network) also considered the participation of Chaser #4, whether the chasers recognized the contestant, and the contestant's gender as important features. For this model, the participation of Chaser #3 and the NUTS 2 region were also considered as important. For all the models, the participation of the Chaser #4 pushed the model's output toward predicting that a contestant would lose the game. Intuitively, longer time and more prize money in the selected offer also had the same impact. Interestingly, the influence of the gender on the neural network was opposite to its influences on the SVM and kNN models. Although in the neural network, a higher gender value pushed the model toward a positive prediction of a contestant winning the game, in the other two  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Method** & **SMOTE** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline Support Vector & + & 0.68571 & 0.65369 & 0.68571 & 0.65432 \\ Machine & - & 0.65714 & 0.65714 & 0.65714 & 0.65714 \\ K-Nearest Neighbors & - & **0.76923** & **0.73133** & **0.76923** & **0.74563** \\ Pruned Decision Tree & + & 0.74286 & 0.72976 & 0.74286 & 0.73012 \\ Voting Classifier & + & 0.77143 & 0.77714 & 0.77143 & 0.74127 \\ Random Forest & + & 0.71429 & 0.69388 & 0.71429 & 0.69353 \\ XGBoost & + & 0.71429 & 0.69143 & 0.71429 & 0.67659 \\ \hline \hline \end{tabular}
\end{table}
Table 13: Outcome prediction based on the profile, cash builder result, all the offers, and specific chasers within the selected offer.  
models, higher values for this feature shifted the prediction toward lower values, predicting that a contestant would lose the game.