numerical.predictive.power

Function that graphs the density of individuals and shows their category according to a numerical variable.

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.

numerical.predictive.power function

<dl><dt>data</dt>
<dd>A data frame.</dd>
<dt>predict.variable</dt>
<dd>Character type. The name of the variable to predict. This name must be part of the columns of the data frame.</dd>
<dt>variable.to.compare</dt>
<dd>Character type. The name of the numeric variable to compare. This name must be part of the columns of the data frame.</dd>
<dt>ylab</dt>
<dd>A character string that describes the y-axis on the graph.</dd>
<dt>xlab</dt>
<dd>A character string that describes the x-axis on the graph.</dd>
<dt>main</dt>
<dd>Character type. The main title of the chart.</dd>
<dt>col</dt>
<dd>A vector that specifies the colors of the categories of the variable to predict.</dd></dl>

Arguments

numerical.predictive.power — numerical.predictive.power

<dl>

<dt>data</dt>
<dd>A data frame.</dd>


<dt>predict.variable</dt>
<dd>Character type. The name of the variable to predict. This name must be part of the columns of the data frame.</dd>


<dt>variable.to.compare</dt>
<dd>Character type. The name of the numeric variable to compare. This name must be part of the columns of the data frame.</dd>


<dt>ylab</dt>
<dd>A character string that describes the y-axis on the graph.</dd>


<dt>xlab</dt>
<dd>A character string that describes the x-axis on the graph.</dd>


<dt>main</dt>
<dd>Character type. The main title of the chart.</dd>


<dt>col</dt>
<dd>A vector that specifies the colors of the categories of the variable to predict.</dd>

</dl>

numerical.predictive.power: numerical.predictive.power

Description

Usage

Value

Arguments

See Also

Examples