## 3 Results
### Application of the Methodology
#### 3.2.2 Analysis
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**TP Rate**} & \multicolumn{1}{c}{**FP Rate**} & \multicolumn{1}{c}{**Precision**} & \multicolumn{1}{c}{**Recall**} & \multicolumn{1}{c}{**F-Measure**} & \multicolumn{1}{c}{**MCC**} & \multicolumn{1}{c}{**ROC**} & \multicolumn{1}{c}{**PCR**} & \multicolumn{1}{c}{**Class**} \\  & & & & & & & & & \\ \hline
1.000 & 0.042 & 0.852 & 1.000 & 0.920 & 0.904 & 0.982 & 0.885 & Never \\
0.779 & 0.017 & 0.92 & 0.779 & 0.844 & 0.812 & 0.958 & 0.871 & Little \\
0.934 & 0.025 & 0.908 & 0.934 & 0.921 & 0.900 & 0.966 & 0.884 & Spomething \\
0.569 & 0.121 & 0.542 & 0.569 & 0.555 & 0.439 & 0.81 & 0.470 & Enough \\
0.479 & 0.100 & 0.529 & 0.479 & 0.503 & 0.394 & 0.81 & 0.474 & Always \\ Weighted avg. & 0.755 & 0.060 & 0.754 & 0.755 & 0.752 & 0.694 & 0.906 & 0.720 \\ \hline \hline \end{tabular}  
* TP Rate, the true positive rate, represents the instances correctly classified as a given class. FP Rate, the false positive rate, represents the instances falsely classified as a given class. Precision is the proportion of instances that are true of a class divided by the total number of instances classified as that class. Recall is the proportion of instances classified as a given class divided by the actual total in that class, equivalent to the TP rate. F–Measure is a combined measurement of precision and recall calculated as Precision-Recall/(Precision + Recall). MCC is used in machine learning as a measure of the quality of binary classifications (two classes); it considers true and false positives and negatives and is generally considered to be a balanced measure that can be used even if the classes are of very different sizes. ROC is the measurement of the receiver operating characteristic area; it is one of the most important WEKA output values, and it provides an idea of how classifiers work in general. PRC, the precision recovery area or precision recovery graph, is more informative than the ROC graph when evaluating binary classifiers on unbalanced datasets.  
\end{table} 
Table 15: Detailed accuracy by class, with a training dataset corresponding to 506 instances. Various metrics were used in this investigation to evaluate the performance of the algorithms. The classifier performance implies precision, error rate, recall, and F-Measure. Table 17 presents the detailed precision data by class. In the results obtained from the confusion matrix in Table 18, 46 instances were classified incorrectly, and 171 were correctly identified. In the class that never used eCommerce, there were 40 people classified as correct in the iteration, meaning that their answers in the survey corresponded to their real opinion. Among the erroneous classifications, there were five people, two of whom belonged to group c, two to group d, and one to group e. Of people who used eCommerce little, 29 were classified correctly, and 8 were incorrect. In the class something, there were 41 correct and 4 incorrect classifications. In the class enough, 25 classifications were correct and 21 incorrect; finally, in the class always, 36 people were classified as correct and 5 as incorrect. With these results, it was possible to determine for each class the number of effective surveys that allowed us to evaluate the use of eCommerce; regarding the number of incorrect classifications, a relatively low value was obtained, which could be considered in another analysis for the identification of the reasons why the answers did not represent reality in the categorized classes. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Stratified Cross-Validation** \\ \hline Correctly classified instances & 171 & 78.8018\% \\ Incorrectly classified instances & 46 & 21.1982\% \\ Kappa statistic & 0.7347 & \\ Mean absolute error & 0.097 & \\ Root mean square error & 0.2838 & \\ Relative absolute error & 30.356\% & \\ Relative root square error & 70.9858\% & \\ Total number of instances & 217 & \\ \hline \hline \end{tabular}
\end{table}