## 7 Conclusions  
In this paper, we review the most frequent ML libraries used in HEP, checking their popularity and comparing their performance in terms of signal-background discrimination in an independent simulated dataset. These datasets are generated and selected using the LHCb detector at CERN as a benchmark. In the first part of the paper, we observe that, even though TMVA is one of the most popular libraries, and by far the more frequently used for this type of analyses, there are other options that can work as well or even better. In fact, Sklearn performs better than TMVA in all the decays analysed in this paper when using BDTs boosted with AdaBoost. This can be a way to prompt scientists in HEP collaborations to try new alternatives and generate new content using the most modern libraries that are available according to Python and other languages. Thanks to the wide range of new libraries, the conversion between the ROOT files and Python-friendly data structures is growing easier over time, and this can result in the popularization of these modern ML libraries for particle physicists. In the second part of the paper, we compared the obtained results with some of the most popular ML libraries in data science, namely, Keras, PyTorch and Sklearn, and showed how NNs can improve the results obtained by BDTs. Even if the results are similar between the three of them, PyTorch tends to provide the best scores. In any case, the final choice might depend on other aspects, such as the amount of statistics available, since, for instance, Keras appears to be more robust for lower training statistics. Regarding the selection of features, this depends on the library, but, as a rule of thumb, we recommend not enlarging the list unnecessarily, focusing on high-level libraries that are known to provide excellent signal-background discrimination. To our knowledge, this is the first time that a detailed study of the dependence of algorithms and libraries on the training statistics and number of features has been performed with an HEP dataset. We should highlight that, even though we are able to see some differences among the libraries, algorithms and decays, all AUC scores are in the range of 0.95-0.96, and subject to statistical fluctuations. This means that the results we have obtained are indicative, but must still be made with caution. Therefore, even if our findings can be used as a guideline, we still advise analysts to check for the best classifier for their specific case, depending on aspects such as available statistics or the available CPU for training. As a final note, we emphasize the need for particle physicists to enrich their perspective by looking at what is being carried out in other fields in terms of classification by means of ML. This not only concerns other areas of physics [68], but also fields that look far away, such as medicine [69], chemistry [70], industrial applications [71; 72] or the interface with users [73]. Conceptualization, X.C.V., L.D.M. and A.D.S. ; methodology, X.C.V., L.D.M. and A.D.S. ; software, X.C.V., L.D.M. and A.D.S. ; validation, X.C.V., L.D.M. and A.D.S. ; formal analysis, X.C.V., L.D.M. and A.D.S. ; investigation, X.C.V., L.D.M. and A.D.S. ; resources, X.C.V., L.D.M. and A.D.S. ; data curation, X.C.V., L.D.M. and A.D.S. ; writing--original draft preparation, X.C.V., L.D.M. and A.D.S. ; writing--review and editing, X.C.V., L.D.M. and A.D.S. ; visualization, X.C.V., L.D.M. and A.D.S. ; supervision, X.C.V. and A.D.S. ; project administration, X.C.V., L.D.M. and A.D.S. ; funding acquisition, X.C.V., L.D.M. and A.D.S. All authors have read and agreed to the published version of the manuscript. This work has received financial support from Xunta de Galicia (Centro singular de investigacion de Galicia accreditation 2019-2022), by European Union ERDF, and by the \"Maria de Maeztu\" Units of Excellence program MDM-2016-0692 and the Spanish Research State Agency. In particular: the work of X.C.V.