# Classification of Retail Products: From Probabilistic Ranking to Neural Networks
## 9 Evaluation and Results
In addition, the best fit with _Top_2_ is 600 principal \begin{table}
\begin{tabular}{c c c c} \hline \hline  & _Top\_1_ & _Top\_2_ & _Top\_3_ \\ \hline
**Accuracy** & 0.833 & 0.920 & 0.948 \\
**Precision** & 0.829 & 0.914 & 0.937 \\
**Recall** & 0.824 & 0.905 & 0.932 \\
**F1-score** & 0.818 & 0.905 & 0.931 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Score-based Methods: Quality measures. Figure 6: Score-based Methods: Quality measures. components with accuracy (0.9153), also suitable for the F1-score (0.8302). In addition to the _Top_3_, the best result is 600 principal components with accuracy (0.9413) and F1-score (0.8302). We would like to draw attention to the values of _Top_1_ results in Tables 9 and 10. Both of them are the same, which indicates that recall equals precision and accuracy. This means that the model is somehow \"balanced\", that is, its ability to correctly classify positive samples (_TP+FN_) is the same as its ability to correctly classify negative samples (_TN+FP_). Therefore, the F1-score is also the same [53]. Table 11 presents the performance of our XGBoost classifier, which was also obtained for the principal components (400, 500, 600, 700, and 800), which declares selecting 400 as the best value for the principal component for _Top_1_, _Top_2_ and _Top_3_; accuracy 0.7071 for _Top_1_; 0.8126 for _Top_2_ and 0.8568 for _Top_3_. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**PCA** & **400** & **500** & **600** & **700** & **800** \\ \hline \multicolumn{6}{c}{_Top_1_} \\ \hline
**Accuracy** & 0.8396 & 0.8424 & 0.8446 & 0.8443 & 0.8459 \\
**Precision** & 0.8396 & 0.8424 & 0.8446 & 0.8443 & 0.8459 \\
**Recall** & 0.8396 & 0.8424 & 0.8446 & 0.8443 & 0.8459 \\
**F1-score** & 0.8396 & 0.8424 & 0.8446 & 0.8443 & 0.8459 \\ \hline \multicolumn{6}{c}{_Top_2_} \\ \hline
**Accuracy** & 0.8957 & 0.9141 & 0.9153 & 0.9061 & 0.9096 \\
**Precision** & 0.7908 & 0.8289 & 0.8317 & 0.8165 & 0.8216 \\
**Recall** & 0.7885 & 0.8266 & 0.8288 & 0.8126 & 0.8177 \\
**F1-score** & 0.7897 & 0.8278 & 0.8302 & 0.8146 & 0.8196 \\ \hline \multicolumn{6}{c}{_Top_3_} \\ \hline
**Accuracy** & 0.9315 & 0.94 & 0.9413 & 0.9362 & 0.9362 \\
**Precision** & 0.7949 & 0.8291 & 0.8319 & 0.8129 & 0.8167 \\
**Recall** & 0.7803 & 0.8231 & 0.8285 & 0.8088 & 0.8123 \\
**F1-score** & 0.7875 & 0.8261 & 0.8302 & 0.8109 & 0.8145 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: FKNN: Quality measures by PCA components. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**PCA** & **400** & **500** & **600** & **700** & **800** \\ \hline \multicolumn{6}{c}{_Top_1_} \\ \hline
**Accuracy** & 0.8339 & 0.8348 & 0.8367 & 0.8427 & 0.8332 \\
**Precision** & 0.8339 & 0.8348 & 0.8367 & 0.8427 & 0.8332 \\
**Recall** & 0.8339 & 0.8348 & 0.8367 & 0.8427 & 0.8332 \\
**F1-score** & 0.8339 & 0.8348 & 0.8367 & 0.8427 & 0.8332 \\ \hline \multicolumn{6}{c}{_Top_2_} \\ \hline
**Accuracy** & 0.8862 & 0.8871 & 0.8874 & 0.8871 & 0.8865 \\
**Precision** & 0.8405 & 0.8441 & 0.8403 & 0.8408 & 0.8346 \\
**Recall** & 0.8019 & 0.805 & 0.8072 & 0.8072 & 0.8063 \\
**F1-score** & 0.8207 & 0.8241 & 0.8234 & 0.8237 & 0.8202 \\ \hline \multicolumn{6}{c}{_Top_3_} \\ \hline
**Accuracy** & 0.9201 & 0.9125 & 0.9141 & 0.915 & 0.9137 \\
**Precision** & 0.8302 & 0.8313 & 0.8282 & 0.8048 & 0.8235 \\
**Recall** & 0.78 & 0.7793 & 0.7873 & 0.7844 & 0.7841 \\
**F1-score** & 0.8043 & 0.8045 & 0.8072 & 0.8263 & 0.8033 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: KNN: Quality measures by number of PCA components. The performance of our MLP classifier was obtained also for 400, 500, 600, 700, and 800 principal components, as it is shown in Table 12, which suggests selecting 800 as the best value for the principal component for _Top_1_, _Top_2_, and _Top_3_; accuracy 0.8388 for _Top_1_; 0.8436 for _Top_2_ and 0.8436 for _Top_3_.