## 3 Results
### Application of the Methodology
#### 3.2.2 Analysis

Table 16: Stratified cross-validation with a training dataset corresponding to 217 instances. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**TP Rate** & **FP Rate** & **Precision** & **Recall** & **F-Measure** & **MCC** & **ROC** & **PCR** & **Area** & **Class** \\ \hline
1.000 & 0.034 & 0.87 & 1.000 & 0.930 & 0.917 & 0.978 & 0.844 & Never \\
0.690 & 0.017 & 0.906 & 0.690 & 0.784 & 0.750 & 0.877 & 0.737 & Little \\
0.854 & 0.101 & 0.707 & 0.854 & 0.774 & 0.707 & 0.874 & 0.591 & Something \\
0.543 & 0.053 & 0.735 & 0.543 & 0.625 & 0.552 & 0.794 & 0.585 & Enough \\
0.878 & 0.063 & 0.766 & 0.878 & 0.818 & 0.775 & 0.929 & 0.712 & Always \\ Weighted avg. & 0.788 & 0.055 & 0.793 & 0.788 & 0.781 & 0.734 & 0.887 & 0.687 \\ \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 2 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 WEA’s 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 17: Detailed accuracy by class. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**a** & **b** & **c** & **d** & **e** & **Classified as** \\ \hline
40 & 0 & 0 & 0 & 0 & a = Never \\
6 & 29 & 7 & 0 & 0 & b = Little \\
0 & 3 & 41 & 4 & 0 & c = Something \\
0 & 0 & 10 & 25 & 11 & d = Enough \\
0 & 0 & 0 & 5 & 36 & e = Always \\ \hline \hline \end{tabular}
\end{table} 
Table 18: Results obtained from the confusion matrix by people classified correctly and incorrectly and the identification of the number of surveys that did not respond to the study phenomenon. The ROC area measurement is one of the most important values of the algorithm output. The optimal classifier had ROC area values close to 1; therefore, the survey or data collection instrument was taken as valid. The next phase of the analysis generated the data cluster to which the SimpleKMeans algorithm was applied. Table 19 presents the results obtained, where two clusters were generated; these evaluated the data when the users indicated that they used an eCommerce channel at the \"Something and Enough\" level. In each cluster, the existing relationships were indicated by a question and how these determined the predetermining factors of the use of eCommerce in the selected population. The table shows that there were 13 attributes, which corresponded to the 12 questions that evaluated the six initial categories. To differentiate the questions, the category and an identifier corresponding to the number of questions were placed, for example, Accessibility-Preg1-Accessibility-Preg2; this was repeated with each question. In attribute analysis, the best first, with a forward search direction, was used as the search method. The process generated 79 evaluated subsets, and the merit of the best subset found was 0.233, with question two of the satisfaction category being the attribute with the highest influence on the use of eCommerce.