## 5 Results
### Classifiers Performance Assessment  
Following the correlation analysis to reveal potential relations among metrics, a classifiers' performance assessment takes place to indicate the classification model for best predictions. To determine the performance of the algorithms for a certain dataset, it is important to take into consideration the nature and the characteristics of the data, its features, and the specific problem context. The actual application of an ML model to the dataset will provide enough evidence to decide on the best solution. According to the scientific literature, DTs can provide advantages when compared to k-NN and NB in various domains. Due to their nature to inherit flexibility and robustness to certain data types and characteristics across domains, they highly perform when processing complex relations among attributes and the class variable. This allows them to be useful to capture the underlying patterns in linear data. DTs perform well both in nominal and numerical \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Class** & **Engagement Level** & **Sessions** & **Instances** \\ \hline
1 & Low engagement & 1 & 601 \\
2 & Medium engagement & 2 & 50 \\
3 & High engagement & \(\geq\)3 & 9 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Class Segmentation. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Variable Pair** & **Spearman Correlation** & \(p\)**-Value** \\ \hline Event count and sessions & 0.2931 & 0.000 \\ Purchase revenue and sessions & \(-\)0.0274 & 0.483 \\ Transactions and conversions & 1.0000 & 0.000 \\ Bounce rate and purchase revenue & \(-\)0.0822 & 0.034 \\ Event count and conversions & 0.1130 & 0.003 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Correlation analysis. attributes, providing the ability for a more intuitive interpretation and analysis, similar to human decision-making processes. Due to their hierarchical structure, their interpretability is considered an advantage when the decision logic is equally essential as the decision itself. Further on, compared to k-NN and NB, DTs are insensitive to outliers and missing attribute values. The first can be easily affected by anomalies due to their approach, which relies on guesses about the distribution of data. While k-NN and NB may suffer from data dimensionality, they tend to perform computationally intensively with large datasets. DTs manage to decrease that risk using feature selection and dimensionality reduction at each split [11; 13; 15; 16]. Nevertheless, DTs are not perfect, as they are prone to the phenomenon of overfitting, especially with very small datasets and complex trees. Techniques like pruning or random forests are essential to overcome these problems. On the contrary, k-NN is a very simple if not the simplest ML algorithm. It does not demand any complex parameter configuration or model training. It works on the basis that similar data spaces are likely to have similar results, making it easy to comprehend. The local nature of the algorithm allows it to adapt to different regions of the data space. NB computes the probability of each class given the input features and then assigns the class with the highest probability. It can be efficient in probabilistic classification tasks like fraud detection despite its simplicity and independent hypotheses [11; 13; 15; 16]. Table 9 and Figure 8 represent the overall classification results for predicting the conversions using DTs, NB, and k-NN classifiers for class sessions. Figure 8 visualizes an overall classification performance assessment among classifiers DTs, NB, and k-NN, including metrics such as accuracy, precision, recall, and F1-score. Precision represents the proportion of true positive (TP) predictions in all positive predictions (P) for the minority class. Recall assesses the classifier's ability to correctly identify all instances of the minority class. F1-score signifies a balance between two metrics, precision and recall, for the minority class, providing a single assessment performance metric when measuring both metrics.