## 6 Conclusions
### Result Overview
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Model** & **SMOTE** & **Scenario** & **Parameters** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline \multirow{4}{*}{K-Nearest Neighbors} & + & 1 & k = 1, p = 1 & 0.65714 & 0.66889 & 0.65714 & 0.66060 \\  & + & 2 & k = 1, p = 1 & 0.57143 & 0.61544 & 0.57143 & 0.59014 \\  & + & 3 & k = 5, p = 1 & 0.71429 & 0.70846 & 0.71429 & 0.69420 \\ \hline \multirow{2}{*}{Gaussian Naive Bayes} & - & 1 & - & 0.65714 & 0.64571 & 0.65714 & 0.63746 \\  & - & 2 & - & 0.62857 & 0.61363 & 0.62857 & 0.60114 \\ \hline Neural Network & + & 1 & * & 0.65385 & 0.54049 & 0.65385 & 0.59050 \\ \hline Decision Tree & + & 3 & - & 0.71429 & 0.71683 & 0.71429 & 0.71071 \\ \hline \multirow{2}{*}{Pruned Decision Tree} & + & 3 & \begin{tabular}{c} ccp\_alpha \\ = 0.0068 \\ \end{tabular} & 0.71429 & 0.71091 & 0.71429 & 0.70899 \\ \hline \multirow{4}{*}{Voting Classifier} & + & 1 & 3 models * & 0.65714 & 0.66889 & 0.65714 & 0.66060 \\  & + & 2 & 3 models * & 0.60000 & 0.62778 & 0.60000 & 0.61210 \\ \cline{1-1}  & + & 3 & 5 models * & 0.74286 & 0.75202 & 0.74286 & 0.73642 \\ \hline Random Forest & - & 3 & \begin{tabular}{c} estim = 8 \\ \end{tabular} & 0.65714 & 0.64935 & 0.65714 & 0.65244 \\ \hline \multirow{4}{*}{XGBoost} & + & 1 & \begin{tabular}{c} estim = 4, \\ lr = 0.5 \\ \end{tabular} & 0.57143 & 0.54731 & 0.57143 & 0.55502 \\  & + & 2 & \
\begin{tabular}{c} estim = 16, \\ lr = 0.5 \\ \end{tabular} & 0.57143 & 0.53714 & 0.57143 & 0.54216 \\  & - & 3 &
\begin{tabular}{c} estim = 29, \\ lr = 0.2 \\ \end{tabular} & 0.68571 & 0.66044 & 0.68571 & 0.65110 \\ \hline \hline \end{tabular}
\end{table} 
Table 18: Summary of the selected offer prediction results (parameters marked with * are described in detail in the corresponding subsections of Section 5.2). The contestant's profile;
2. The contestant's profile, the cash builder result, all the offers, and specific chasers within the selected offer;
3. The cash builder result, all the offers, and specific chasers within the selected offer;
4. The selected number of chasers and the cash builder result;
5. The selected number of chasers and the chasers' time in the selected offer;
6. The selected number of chasers, the cash builder result, and the chasers' time in the selected offer. The results, parameters, and use of SMOTE in all the models and scenarios are summarized in Table 19. In the majority of the scenarios, the neural network yielded the best result, reaching a prediction accuracy of as high as 84.6% for the combination of the number of chasers and the chasers' time in the selected offer (5). On average, the best performance was reached when also including the cash builder result (6). We also utilized SHAP values to interpret the models' decisions in the outcome prediction task. As the best-performing models were the ones in the last three scenarios, which used only the selected number of chasers, the cash builder result, and the chasers' time in the selected offer, we emphasize these features as the most impactful ones. In most cases, higher values for these features led the models to predict that the contestant would lose the game, while lower values pushed the models' outputs to decide the opposite. The best-performing model in almost all the cases, the neural network, was the only model where the mid-range value for the cash builder result pushed the model's output toward predicting that a contestant would win, while its lower values shifted the model's prediction toward a negative outcome for the contestant. Regarding the other features, the participation of Chaser #4, the contestant's gender, and whether the chasers recognized the contestant were the most impactful features. In the best-performing model (the neural network), a higher value for gender and not being recognized by the chasers led the model's outputs to predict that a contestant would win the game. The participation of Chaser #4 was also considered as important by several models, with their participation shifting the model's output to lower values.