imputation_predictive

Base class for supervised imputers that learn one target column from a set of source 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 Sa

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 

imputation_predictive function

<dl><dt>target</dt>
<dd>target column to impute</dd>
<dt>sources</dt>
<dd>optional vector of predictor column names</dd>
<dt>method</dt>
<dd>initial imputation method for numeric source columns: "median" or "mean"</dd></dl>

Arguments

Predictive imputation base — imputation_predictive

<dl>

<dt>target</dt>
<dd>target column to impute</dd>


<dt>sources</dt>
<dd>optional vector of predictor column names</dd>


<dt>method</dt>
<dd>initial imputation method for numeric source columns: "median" or "mean"</dd>

</dl>

Predictive imputation base

imputation_predictive: Predictive imputation base

Description

Usage

Value

Arguments

Details

Examples