traineR

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.

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