20.500.12556/RUL-133715 On the interpretability of machine learning models and experimental feature selection in case of multicollinear data In the field of machine learning, a considerable amount of research is involved in the interpretability of models and their decisions. The interpretability contradicts the model quality. Random Forests are among the best quality technologies of machine learning, but their operation is of “black box” character. Among the quantifiable approaches to the model interpretation, there are measures of association of predictors and response. In case of the Random Forests, this approach usually consists of calculating the model’s feature importances. Known methods, including the built-in one, are less suitable in settings with strong multicollinearity of features. Therefore, we propose an experimental approach to the feature selection task, a greedy forward feature selection method with least-trees-used criterion. It yields a set of most informative features that can be used in a machine learning (ML) training process with similar prediction quality as the original feature set. We verify the results of the proposed method on two known datasets, one with small feature multicollinearity and another with large feature multicollinearity. The proposed method also allows for a domain expert help with selecting among equally important features, which is known as the human-in-the-loop approach. interpretable machine learning feature multicollinearity random forests feature selection feature importance greedy feature selection razložljivo strojno učenje multikolinearnost značilk naključni gozdovi izbira značilk pomembnost značilk požrešna izbira značilk true false true Angleški jezik Slovenski jezik Članek v reviji 2021-12-10 13:16:13 2021-12-10 13:16:14 2023-05-25 09:06:24 0000-00-00 00:00:00 2020 0 0 15 str. iss. 5, art. 761 Vol. 9 May 2020 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 004.8 2079-9292 10.3390/electronics9050761 14438659 RAZ_Drobnic_Franc_2020.pdf RAZ_Drobnic_Franc_2020.pdf 1 E803CFED6E182F33E8A85BAFDF1E72A6 b9b828908b93c927da57d9f394858b5965d41c0b87bbdad1adee1dc146f55c2b 334b0346-59b3-11ec-abdb-00155dcfd717 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=152013 https://www.mdpi.com/2079-9292/9/5/761 1 db243cbf-59b2-11ec-abdb-00155dcfd717 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=152012 Fakulteta za elektrotehniko 0 0 0