contr.dummy

Returns a matrix of contrasts for the <code>train.kknn</code>.

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>.

Oldemar Rodriguez

traineR

Predictive (Classification and Regression) Models Homologator

Oldemar Rodriguez R.

Andres Navarro D.

Ariel Arroyo S.

Diego Jimenez A.

Jennifer Lobo V.

contr.dummy function

<dl><dt>n</dt>
<dd>A vector containing levels of a factor, or the number of levels.</dd>
<dt>contrasts</dt>
<dd>A logical value indicating whether contrasts should be computed.</dd></dl>

Arguments

contr.dummy — contr.dummy

<dl>

<dt>n</dt>
<dd>A vector containing levels of a factor, or the number of levels.</dd>


<dt>contrasts</dt>
<dd>A logical value indicating whether contrasts should be computed.</dd>

</dl>

contr.dummy: contr.dummy

Description

Usage

Value

Arguments