nn_model

data

the name of the variable to be predicted.

variable.pred

a vector of integers specifying the number of hidden neurons (vertices) in each layer.

hidden

a numeric value specifying the threshold for the partial derivatives of the error function as stopping criteria.

threshold

the maximum steps for the training of the neural network. Reaching this maximum leads to a stop of the neural network's training process.

stepmax

generates the code to create the neural network model.

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.

nn_model function

<dl><dt>data</dt>
<dd>dataframe</dd>
<dt>variable.pred</dt>
<dd>the name of the variable to be predicted.</dd>
<dt>hidden</dt>
<dd>a vector of integers specifying the number of hidden neurons (vertices) in each layer.</dd>
<dt>threshold</dt>
<dd>a numeric value specifying the threshold for the partial derivatives of the error function as stopping criteria.</dd>
<dt>stepmax</dt>
<dd>the maximum steps for the training of the neural network. Reaching this maximum leads to a stop of the neural network's training process.</dd></dl>

Arguments

nn_model — nn_model

<dl>

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


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


<dt>hidden</dt>
<dd>a vector of integers specifying the number of hidden neurons (vertices) in each layer.</dd>


<dt>threshold</dt>
<dd>a numeric value specifying the threshold for the partial derivatives of the error function as stopping criteria.</dd>


<dt>stepmax</dt>
<dd>the maximum steps for the training of the neural network. Reaching this maximum leads to a stop of the neural network's training process.</dd>

</dl>

nn_model: nn_model

Description

Usage

Arguments

See Also