predict.bayes.prmdt

Return prediction for a <code><a href="/link/naiveBayes?package=traineR&version=2.2.11" data-mini-rdoc="traineR::naiveBayes">naiveBayes</a></code> model.

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.

predict.bayes.prmdt function

<dl><dt>object</dt>
<dd>a <code><a href="/link/naiveBayes?package=traineR&version=2.2.11" data-mini-rdoc="traineR::naiveBayes">naiveBayes</a></code> model object for which prediction is desired.</dd>
<dt>newdata</dt>
<dd>an optional data frame in which to look for variables with which to predict.</dd>
<dt>type</dt>
<dd>type of prediction 'prob' or 'class' (default).</dd>
<dt>threshold</dt>
<dd>Value replacing cells with 0 probabilities.</dd>
<dt>eps</dt>
<dd>double for specifying an epsilon-range to apply laplace smoothing (to replace zero or close-zero probabilities by theshold).</dd>
<dt>...</dt>
<dd>additional arguments affecting the predictions produced.</dd></dl>

Arguments

Return prediction for a <code><a href='https://rdrr.io/pkg/e1071/man/naiveBayes.html'>naiveBayes</a></code> model.

predict.bayes.prmdt — predict.bayes.prmdt

<dl>

<dt>object</dt>
<dd>a <code><a href='https://rdrr.io/pkg/e1071/man/naiveBayes.html'>naiveBayes</a></code> model object for which prediction is desired.</dd>


<dt>newdata</dt>
<dd>an optional data frame in which to look for variables with which to predict.</dd>


<dt>type</dt>
<dd>type of prediction 'prob' or 'class' (default).</dd>


<dt>threshold</dt>
<dd>Value replacing cells with 0 probabilities.</dd>


<dt>eps</dt>
<dd>double for specifying an epsilon-range to apply laplace smoothing (to replace zero or close-zero probabilities by theshold).</dd>


<dt>...</dt>
<dd>additional arguments affecting the predictions produced.</dd>

</dl>

predict.bayes.prmdt: predict.bayes.prmdt

Description

Usage

Value

Arguments