## 5 Research Results
### Prediction Results of Multilayer Perceptron (MLP) and Radial Basis Function (RBF) Models
A detailed analysis of both graphs should be started from the left side, that is, from the answer \"I would accept\". The first box plot on the left indicates the answer \"I would accept\", while the second box plot shows the probability that \"I wouldn't accept\" would be classified as an \"I would accept\" answer, even though it actually falls under the answer \"I wouldn't accept\". These results indicate that the MLP model (see Figure 4a) better correctly classified the answers in both classes compared to the RBF model, which gave worse results, especially when predicting the \"I wouldn't accept\" class (see Figure 4b). \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Model** & **Accuracy** & **FP Rate** & **Precision** & **Recall** & **F1** & **MCC** & **ROC** & **Class** \\ \hline Multilayer & 0.887 & 0.375 & 0.913 & 0.950 & 0.931 & 0.618 & 0.927 & I would accept \\ \cline{2-9} Perceptron (MLP) & 0.887 & 0.050 & 0.750 & 0.625 & 0.682 & 0.618 & 0.927 & I wouldn’t accept \\ \cline{2-9}  & 0.887 & 0.213 & 0.832 & 0.788 & 0.807 & 0.618 & 0.927 & Avg. \\ \hline \multirow{3}{*}{Radial Basis Function (RBF)} & 0.805 & 0.522 & 0.870 & 0.889 & 0.879 & 0.380 & 0.897 & I would accept \\ \cline{2-9}  & 0.805 & 0.111 & 0.524 & 0.478 & 0.500 & 0.380 & 0.897 & I wouldn’t accept \\ \cline{1-1} \cline{2-9}  & 0.805 & 0.316 & 0.697 & 0.684 & 0.690 & 0.380 & 0.897 & Avg. \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Accuracy of e-bike acceptability classification. Also, a visual representation of the classification performed by the models relative to the correct classifications that could be random outcomes (i.e., without using the model) is shown in Figure 5. The performance of the multilayer perceptron model on the gain plot (see Figure 5a) illustrates that the \"I wouldn't accept\" class, in terms of the first point, was at (10%, 41%), which indicates that the first 10% covers slightly more than 40% of all users who answered that they would not accept the use of e-bikes. This performance is better compared to the performance of the radial basis function where on the gain graph (see Figure 5b), in terms of the first point, the same class was at (10%, 37%). On the other hand, looking at the first point of the \"I would accept\" class, it can be observed that both models have identical performance (10%, 12%), indicating that the first 10% covers slightly less than 15% of all users who answered that they would accept the use e-bikes. The lift curve is the basis for evaluating the performance of classification models. However, unlike the confusion matrix, which evaluates the model for the entire population, growth curves evaluate the performance of the model only in a subset of the population. That is, they use a piece of data to give clear insight into the advantages of using a model as opposed to not using a model. The values from the gain charts of the MLP and RBF  
Figure 4: Predicted pseudo-probability presented for responses to the e-bicycle acceptability question: (**a**) box plot diagram of MLP model; (**b**) box plot diagram of RBF model. Figure 5: Presentation of the correct classifications obtained by the model in relation to classifications without the use of the model: (**a**) gain curves of multilayer perceptron model and (**b**) gain curves of radial basis function model. models (Figure 5a,b) were used to calculate the lift values. In this regard, the increase of 41% for the group of users who responded with \"I wouldn't accept\" is 41%/10% = 4.1 for the MLP model (Figure 6a), while the increase of 37% for the group of users who answered with \"I wouldn't accept\" is 37%/10% = 3.7 for the RBF model (Figure 6b). Bearing in mind that both models had the same point value on the gain curves, this means that in both cases, an increase of 12% for the group of users who answered with \"I would accept\" is 12%/10% = 1.2. The slopes shown in Figure 5 decrease steadily because there are fewer records of interesting classes to add and the model has less potential for bias, as seen in the lift graph.