boosting_model

data

the name of the variable to be predicted.

variable.pred

integer specifying the total number of trees to fit.

n.trees

either a character string specifying the name of the distribution to use or a list with a component name specifying the distribution and any additional parameters needed.

distribution

the shrinkage parameter of the model. The learning rate or step-size reduction

shrinkage

Perform a supervised data analysis on a database through a 'shiny' graphical interface. It includes methods such as linear regression, penalized regression, k-nearest neighbors, decision trees, ada boosting, extreme gradient boosting, random forest, neural networks, deep learning and support vector machines.

Oldemar Rodriguez

regressoR

Regression Data Analysis System

Oldemar Rodriguez R.

Andres Navarro D.

Diego Jimenez A.

Ariel Arroyo S.

boosting_model function

<dl><dt>data</dt>
<dd>dataframe</dd>
<dt>variable.pred</dt>
<dd>the name of the variable to be predicted.</dd>
<dt>n.trees</dt>
<dd>integer specifying the total number of trees to fit.</dd>
<dt>distribution</dt>
<dd>either a character string specifying the name of the distribution to use or a list with a component name specifying the distribution and any additional parameters needed.</dd>
<dt>shrinkage</dt>
<dd>the shrinkage parameter of the model. The learning rate or step-size reduction</dd></dl>

Arguments

boosting_model — boosting_model

<dl>

<dt>data</dt>
<dd>dataframe</dd>


<dt>variable.pred</dt>
<dd>the name of the variable to be predicted.</dd>


<dt>n.trees</dt>
<dd>integer specifying the total number of trees to fit.</dd>


<dt>distribution</dt>
<dd>either a character string specifying the name of the distribution to use or a list with a component name specifying the distribution and any additional parameters needed.</dd>


<dt>shrinkage</dt>
<dd>the shrinkage parameter of the model. The learning rate or step-size reduction</dd>

</dl>

boosting_model: boosting_model

Description

Usage

Arguments

See Also