Data Mining with Rattle and R: The Art of Excavating Data for Knowledge Discovery

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GIS-based landslide susceptibility mapping using numerical risk factor bivariate model and its ensemble with linear multivariate regression and boosted regression tree algorithms (2019, methodology): “…as follows (Friedman 2001): ; ∑ h x; (3) where ; represents a simple classification function with parameter a and variables x, m is the variable that represents the stage of the model and is the weighting coefficient in stage m. BRT was run with R 3.3.1 and the “Rattle” package (Williams 2011).”
The Five Factor Model of personality and evaluation of drug consumption risk (2015, methodology): “Random Forest algorithm builds hundreds of decision trees and combines them into a single model (Williams, 2011).”
mlr: Machine Learning in R (2016, methodology): “…provide frameworks for handling prediction models, including caret (Kuhn, 2008), DMwR (Torgo, 2010), CORElearn (Robnik-Sikonja and with contributions from John Adeyanju Alao, 2016), rattle (Williams, 2011), rminer (Cortez, 2010), CMA (Slawski et al., 2008), and ipred (Peters and Hothorn, 2015).”
Applied Predictive Modeling (2013, methodology): “com Rattle (Williams 2011) is a graphical user interface for R predictive models.”