Machine learning plays a major role in many scientific investigations. However, non-expert programmers may struggle to implement the elaborate pipelines necessary to build highly accurate and generalizable models. We introduce here a new R package, exprso, as an intuitive machine learning suite designed specifically for non-expert programmers. Built primarily for the classification of high-dimensional data, exprso uses an object-oriented framework to encapsulate a number of common analytical methods into a series of interchangeable modules. This includes modules for feature selection, classification, high-throughput parameter grid-searching, elaborate cross-validation schemes (e.g., Monte Carlo and nested cross-validation), ensemble classification, and prediction. In addition, exprso provides native support for multi-class classification through the 1-vs-all generalization of binary classifiers. In contrast to other machine learning suites, we have prioritized simplicity of use over expansiveness when designing exprso.
Evidential multi-class classification from binary classifiers: application to waste sorting quality control from hyperspectral data