# Make predictions from new data. y_pred = clf.predict(X_test),
## 8 Discussion and Conclusions  
In this article, the application of machine learning algorithms to correlate data obtained from MWD with dynamic penetrometer values has been studied. Decision tree algorithms and random forests have been used to predict penetrometer values, unlike in other studies where neural networks have been used. These algorithms have a simpler implementation as few parameters must be adjusted and these are easy to interpret. The graphical representation of the results is even possible, although in this case, it has not been practical due to the large number of resulting nodes. This simplicity, together with the existence of libraries such as scikit-learn ([PERSON] et al., 2014), allow an easier application. The study has been carried out from real execution data. It is not common to have data from sites, as most of the previous studies use laboratory data or historical cases from the bibliography. In addition, many of the existing cases with MWD data are from rock drilling and not soils, as in this article. The results obtained have shown that, for the four sites studied, a fully developed decision tree is capable of achieving great precision. The importance of taking depth into account in the calculations has also been shown since it significantly increases the degree of correlation. It should be noted, however, that the number of penetrometer tests available is low and giving excessive importance to depth could mask terrain of a difference in nature if the soil profile is not homogeneous within the site. By means of a reasonably simple mechanism to implement, a very high level of security is achieved in terms of the soil traversed. This would allow the execution to be adapted to the real conditions of the terrain, as well as to detect possible anomalies. The results obtained, being penetrometer values, are easy to interpret by any geotechnical engineer as this is one of the most common tests and there are various accurate correlations with other soil parameters. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Site** & **Model** & **Set** & **Accuracy** & **Correlation Coefficient** & **MAE** & **MSE** & **R\({}^{2}\)** \\ \hline \multirow{4}{*}{1, 2, 3} & Fully developed & \multirow{2}{*}{Test} & \multirow{2}{*}{0.35 (\(\pm\)0.03)} & 0.82 & \multirow{2}{*}{1.92 (\(\pm\)0.10)} & \multirow{2}{*}{11.84 (\(\pm\)0.18)} & \multirow{2}{*}{0.69 (\(\pm\)0.03)} \\  & DT with depth & & & (\(\pm\)0.02) & & \\  & RF with depth & & & 0.88 & \multirow{2}{*}{1.52 (\(\pm\)0.07)} & \multirow{2}{*}{7.77 (\(\pm\)0.83)} & \multirow{2}{*}{0.80 (\(\pm\)0.02)} \\  & & & & (\(\pm\)0.01) & & \\ \hline \hline \end{tabular}
\end{table} 
Table 15: Correlations obtained for the set of sites 1, 2 and 3. Figure 20: Real and calculated values for site 1, 2 and 3 considering depth. Moreover, this methodology is a more flexible tool than the classical analytical formulas shown in Table 1. In addition, it is automatically adapted to the case under study, making it much more precise than said formulas. The sites analyzed have been drilled with soil displacement. However, the methodology should be applicable to any type of drilling with parameter registration. The methodology has shown equally good results when analyzing a set of sites. Although the application to different sites requires further analysis, these results seem to show that, maintaining the conditions of the equipment and with a sufficient amount of data, it would be possible to train a general algorithm applicable to any site. For future developments, another possible approach to this problem is to use a hybrid method combining supervised and unsupervised learning, initially dividing the data into clusters and from there predicting the target variable [46; 47; 48]. Conceptualization, E.M.G., A.A.A.A. and M.G.A. ; methodology, E.M.G., A.A.A. and M.G.A. ; software, E.M.G. ; validation, A.A.A.A. and M.G.A. ; investigation, E.M.G. ; data curation, E.M.G. ; writing--original draft preparation, E.M.G. ; writing--review and editing, E.M.G., A.A.A.A. and M.G.A.