An overview and comparison of machine-learning techniques for classification purposes in digital soil mapping

Machine-learning is the automated process of uncovering patterns in large datasets using computer-based statistical models, where a fitted model may then be used for prediction purposes on new data. Despite the growing number of machine-learning algorithms that have been developed, relatively few studies have provided a comparison of an array of different learners — typically, model comparison studies have been restricted to a comparison of only a few models. This study evaluates and compares a suite of 10 machine-learners as classification algorithms for the prediction of soil taxonomic units in the Lower Fraser Valley, British Columbia, Canada.