traineR:Predictive (Classification and Regression) Models Homologator
Methods to unify the different ways of creating predictive models and their different predictive formats for
classification and regression. It includes methods such as
K-Nearest Neighbors Schliep, K. P. (2004)
<doi:10.5282/ubm/epub.1769>, Decision Trees Leo Breiman, Jerome
H. Friedman, Richard A. Olshen, Charles J. Stone (2017)
<doi:10.1201/9781315139470>, ADA Boosting Esteban Alfaro,
Matias Gamez, Noelia García (2013) <doi:10.18637/jss.v054.i02>,
Extreme Gradient Boosting Chen & Guestrin (2016)
<doi:10.1145/2939672.2939785>, Random Forest Breiman (2001)
<doi:10.1023/A:1010933404324>, Neural Networks Venables, W. N.,
& Ripley, B. D. (2002) <ISBN:0-387-95457-0>, Support Vector
Machines Bennett, K. P. & Campbell, C. (2000)
<doi:10.1145/380995.380999>, Bayesian Methods Gelman, A.,
Carlin, J. B., Stern, H. S., & Rubin, D. B. (1995)
<doi:10.1201/9780429258411>, Linear Discriminant Analysis
Venables, W. N., & Ripley, B. D. (2002) <ISBN:0-387-95457-0>,
Quadratic Discriminant Analysis Venables, W. N., & Ripley, B.
D. (2002) <ISBN:0-387-95457-0>, Logistic Regression Dobson, A.
J., & Barnett, A. G. (2018) <doi:10.1201/9781315182780> and
Penalized Logistic Regression Friedman, J. H., Hastie, T., &
Tibshirani, R. (2010) <doi:10.18637/jss.v033.i01>.
