## 5 In the Search for a Missing Link
### Selected Offer Predictions
#### 5.2.3 Scenario #3: Contestant's Partial Profile  
Table 11 depicts the models' performances for the third case, where we considered the prediction of the selected offer based on a subset of the information about a contestant (i.e., a partial profile). As there was a high number of missing values, we tried to train the models to predict what a contestant would select based on only the information that was fully available for each contestant. The performances of the GNB and neural network methods dropped by 17% and 7% percent, respectively, but reducing the number of features led to improved results for several of the other tested models. Several models achieved performance metrics over 70%, but the voting classifier (_soft_ voting) again took the lead. Even though the voting classifier, consisting of three models, also surpassed 70%, the classifier consisting of five models outperformed all the other tested models in all the metrics. The models within it were (1) a kNN model (\(k=5\), \(p=1\)), (2) an MNB classifier, (3) an SVM (\(C=10\), \(gamma=1\)), (4) an RF classifier (127 estimators), and (5) a DT that was not pruned. For the kNN model, we used the same parameters as those in the voting classifier. Figure 6 visualizes the pruned DT (\(ccp\_alpha=0.0068\)) in this scenario. Although the DT without pruning performed slightly better, the tree was too complex to present the tree's structure in the paper. Figure 7 shows the confusion matrices for the best-performing model (voting classifier) and the model visualized in Figure 6 (pruned DT). Both models accurately recognized the contestants who selected the offer with four chasers, each misclassifying only three instances. Regarding the other classes, both models frequently predicted that a contestant would select the offer with four chasers when they had actually selected the offer with three. In cases where the true selected offer was the one with five chasers, the voting classifier performed better and misclassified only two instances. On the contrary, in such cases, the pruned DT often predicted the selection of only three chasers. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Method** & **SMOTE** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline K-nearest Neighbors & + & 0.71429 & 0.70846 & 0.71429 & 0.69420 \\ Decision Tree & + & 0.71429 & 0.71683 & 0.71429 & 0.71071 \\ Pruned Decision Tree & + & 0.71429 & 0.71091 & 0.71429 & 0.70899 \\
**Voting Classifier** & + & **0.74286** & **0.75202** & **0.74286** & **0.73642** \\ Random Forest & - & 0.65714 & 0.64935 & 0.65714 & 0.65244 \\ XGBoost & - & 0.68571 & 0.66044 & 0.68571 & 0.65110 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Prediction of the selected offer based on the contestant’s partial profile (gender, age group, hometown size, and NUTS 2 region). Figure 6: Pruned decision tree for the selected offer prediction based on the contestant’s partial profile. Different node colors represent different classes (orange for the offer with 3 chasers, green for the offer with 4 chasers, and violet for the offer with 5 chasers). Darker shades indicate nodes with higher class purity, while lighter shades correspond to nodes with more mixed or balanced class distributions. In this scenario, all the models except for XGBoost highlighted the hometown size as the most important feature and gender as the least important. In the XGBoost model, the situation was reversed, gender was the most important feature, while the hometown size had the least influence. Figure 8 presents the features' impacts on the previously visualized pruned DT model's output for each one of the classes. As visible in the figure, high values for the hometown size and gender tended to push the model to predict that the contestant would choose the offer with three chasers.