dt_pca

PCA (Principal Component Analysis) is an unsupervised
dimensionality reduction technique used in data analysis and
machine learning. It transforms a dataset of possibly
correlated variables into a new set of uncorrelated
variables called principal components.

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

Antonio Castro

Heraldo Borges

Janio Lima

Lucas Tavares

Diego Carvalho

Eduardo Bezerra

Joel Santos

Rafaelli Coutinho

Federal Center for Technological Education of Rio de Janeiro (CEFET/RJ) 

dt_pca function

<dl><dt>attribute</dt>
<dd>target attribute to model building</dd>
<dt>components</dt>
<dd>number of components for PCA</dd></dl>

Arguments

PCA — dt_pca

<dl>

<dt>attribute</dt>
<dd>target attribute to model building</dd>


<dt>components</dt>
<dd>number of components for PCA</dd>

</dl>

dt_pca: PCA

Description

Usage

Value

Arguments

Examples