R.ROSETTA: an interpretable machine learning framework.

ABSTRACT:

Background

Machine learning involves strategies and algorithms that may assist bioinformatics analyses in terms of data mining and knowledge discovery. In several applications, viz. in Life Sciences, it is often more important to understand how a prediction was obtained rather than knowing what prediction was made. To this end so-called interpretable machine learning has been recently advocated. In this study, we implemented an interpretable machine learning package based on the rough set theory. An important aim of our work was provision of statistical properties of the models and their components.

Results

We present the R.ROSETTA package, which is an R wrapper of ROSETTA framework. The original ROSETTA functions have been improved and adapted to the R programming environme

SUBMITTER: Garbulowski M 

PROVIDER: S-EPMC7937228 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature