# Classification of Logging Data Using Machine Learning Algorithms
## 5 Discussion  
The correct identification of reservoir oxidation zones is a very important task in interpreting uranium deposit logging data, which helps to avoid economic losses during the production process. The use of a floating data window allowed us to improve the quality of ROZ classification measured using f1_score_macro by 9% (from 0.65 to 0.74). In solving the ROZ identification problem, the best result (f1_score_macro = 0.744) is demonstrated by the LGBM model, which is also much faster than its closest competitors. In general, the result is 2% better than the one obtained at the previous stage of the research [19]. In the problem of lithological classification, which is part of the uranium mining technology, the best result was achieved using XGBoost (Acc = 0.694, f1_score for class 7 is 0.705). The best result was obtained with the maximum floating window size (200). In general, the use of floating data windows allowed us to improve accuracy by more than 12%. The obtained result is also the best to date compared to the literature data. It exceeds the result mentioned in [49] by 4%.  
It can be assumed that the influence of the floating window in both tasks is due to the fact that classifiers better \"take into account\" the patterns of changes in well logs with increasing window size. This allows us to count on the prospects of using deep learning models. First of all, we are talking about convolutional network models, since this allows us to bring the automatic classification technology closer to experts' assessment, which considers the visual characteristics of logging curves (minimums and maximums, inflection points). Despite the obtained results, which are the best to date, this study has some limitations.  
First, as noted above, we did not use deep learning models.  
Second, we did not solve the problem of finding the optimal set of classifier hyperparameters, which can improve their accuracy indicators.  
Third, we did not evaluate the influence of input parameters on the obtained result, which in principle can allow us to optimize the list of input variables of the model.  
Fourth, although the dataset is quite large, it is significantly smaller than that which can be obtained by an additional selection of logging data.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & & & & & & & & & & & \\ \hline
**Dw\_n** & **Acc** & **1** & **3** & **4** & **5** & **6** & **7** & **9** & **F1\_Macro** & **F1\_Micro** & **Duration** \\ \hline
5 & 0.565 & 0.705 & 0.292 & 0.159 & 0.433 & 0 & 0.623 & 0 & 0.344 & 0.565 & 120.973 \\ \hline
10 & 0.577 & 0.715 & 0.348 & 0.19 & 0.337 & 0.089 & 0.628 & 0 & 0.359 & 0.577 & 114.243 \\ \hline
25 & 0.609 & 0.744 & 0.392 & 0.21 & 0.263 & 0.028 & 0.671 & 0 & 0.364 & 0.609 & 260.255 \\ \hline
50 & 0.636 & 0.768 & 0.415 & 0.243 & 0.301 & 0.005 & 0.683 & 0 & 0.381 & 0.636 & 435.202 \\ \hline
100 & 0.659 & 0.791 & 0.441 & 0.247 & 0.308 & 0 & 0.692 & 0 & 0.391 & 0.659 & 732.618 \\ \hline
200 & **0.694** & 0.819 & 0.492 & 0.295 & 0.28 & 0.008 & **0.705** & 0 & 0.41 & 0.694 & 1319.534 \\ \hline \hline \end{tabular} Note. The best results are highlighted in bold.  
\end{table}
Table 7: Results of k-fold cross-validation (k = 10) for normalized log data in the lithology classification task.