adjust_class_label

One‑hot encode a factor vector into a matrix of indicator columns.

The natural increase in the complexity of current research experiments and data demands better tools to enhance productivity in Data Analytics. The package is a framework designed to address the modern challenges in data analytics workflows. The package is inspired by Experiment Line concepts. It aims to provide seamless support for users in developing their data mining workflows by offering a uniform data model and method API. It enables the integration of various data mining activities, including data preprocessing, classification, regression, clustering, and time series prediction. It also offers options for hyper-parameter tuning and supports integration with existing libraries and languages. Overall, the package provides researchers with a comprehensive set of functionalities for data science, promoting ease of use, extensibility, and integration with various tools and libraries. Information on Experiment Line is based on Ogasawara et al. (2009) <doi:10.1007/978-3-642-02279-1_20>.

Eduardo Ogasawara

daltoolbox

Leveraging Experiment Lines to Data Analytics

Ana Carolina Sá

Antonio Castro

Caio Santos

Diego Carvalho

Diego Salles

Eduardo Bezerra

Esther Pacitti

Fabio Porto

Janio Lima

Lucas Tavares

Rafaelli Coutinho

Rebecca Salles

Vinicius Saidy

CEFET/RJ 

adjust_class_label function

<dl><dt>x</dt>
<dd>vector to be categorized</dd>
<dt>valTrue</dt>
<dd>value to represent true</dd>
<dt>valFalse</dt>
<dd>value to represent false</dd></dl>

Arguments

Adjust categorical mapping — adjust_class_label

<dl>

<dt>x</dt>
<dd>vector to be categorized</dd>


<dt>valTrue</dt>
<dd>value to represent true</dd>


<dt>valFalse</dt>
<dd>value to represent false</dd>

</dl>

Adjust categorical mapping

adjust_class_label: Adjust categorical mapping

Description

Usage

Value

Arguments

Details