Variable importance for sustaining macrophyte presence via random forests: data imputation and model settings.

ABSTRACT: Data sets plagued with missing data and performance-affecting model parameters represent recurrent issues within the field of data mining. Via random forests, the influence of data reduction, outlier and correlated variable removal and missing data imputation technique on the performance of habitat suitability models for three macrophytes (Lemna minor, Spirodela polyrhiza and Nuphar lutea) was assessed. Higher performances (Cohen's kappa values around 0.2-0.3) were obtained for a high degree of data reduction, without outlier or correlated variable removal and with imputation of the median value. Moreover, the influence of model parameter settings on the performance of random forest trained on this data set was investigated along a range of individual trees (ntree), while the number of var

SUBMITTER: Van Echelpoel W 

PROVIDER: S-EPMC6162213 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature