## 5 In the Search for a Missing Link
### Selected Offer Predictions
#### 5.2.1 Scenario #1: Contestant's Profile  
Table 9 shows different models' performances for predicting the offer a contestant will select solely based on their profile. The models with the best performances were the kNN model (\(k=1\), \(p=1\)) and the voting classifier (_soft_ voting), which consisted of three independent classifiers: (1) a kNN model (\(k=1\), \(p=1\)), (2) XGBoost (four estimators, \(lr=0.5\)), and (3) a GNB classifier. Regarding the GNB classifier, we noticed a decay in the performance of as high as 40% when using SMOTE oversampling. For the reported XGBoost result, we used the same parameters as those used when it was included in the voting classifier. In contrast to the other reported methods, its result, presented in Table 9, was obtained without the use of oversampling. The neural network architecture (_batchsize_\(=64\), \(72\) epochs) consisted of five dense layers with sixty-four, thirty-two, sixteen, eight, or three nodes. Each node in the hidden layers used the _ReLU_ activation function, and the output node used _softmax_. After each hidden dense layer, we performed batch normalization and dropout, with rates of 0.05 after the first two layers and 0.01 after the third and fourth.  
In this case, we notice that simpler models, such as kNN or GNB, outperformed complex methods, like XGBoost or neural networks. Although the kNN and voting classifier models reached the same performance level, the kNN model is preferred because  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Method** & **SMOTE** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline
**K-Nearest Neighbors** & + & **0.65714** & **0.66889** & **0.65714** & **0.66060** \\ Gaussian Naive Bayes & - & 0.65714 & 0.64571 & 0.65714 & 0.63746 \\ Neural Network & + & 0.65385 & 0.54049 & 0.65385 & 0.59050 \\
**Voting Classifier** & + & **0.65714** & **0.66889** & **0.65714** & **0.66060** \\ XGBoost & + & 0.57143 & 0.54731 & 0.57143 & 0.55502 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Prediction of the selected offer based on the contestant’s profile.  
of its simplicity and interoperability. Despite a high number of features, which make it difficult to visualize, it is intuitive to understand the model's decision, as it classifies the example in the same class as its nearest neighbors. Additionally, the kNN method is suitable for applying SHAP to gain a deeper insight into the model's decisions.  
Almost all the models used in this scenario recognized the field of work and gender as the most important features. These were followed by the contestant's age group and whether the chasers recognized them from other quizzes. The least important features were the level of education and the intended use of the prize money. In the neural network and XGBoost, the hometown size also had a minor influence on the models' decisions, but as seen in Table 9, these models had the lowest performance metrics among the reported ones. In the GNB classifier and XGBoost, the NUTS 2 region had a weak influence, but in the kNN and neural network models, its importance was higher. In Figure 5, we present the features' impacts for the reported kNN method. As seen in the graph, a higher value for gender led to a decrease in the model's output, while not knowing the chasers beforehand had the opposite effect. A low age-group, lower level of education, and small hometown size also increased the model's output, meaning that these features favor the selection of the offers with more chasers.