Authors: Eduardo Ogasawara, Antonio Castro, Cristiane Gea, Heraldo Borges, Diego Carvalho, Joel Santos, Eduardo Bezerra, Rafaelli Coutinho
Abstract: The natural increase in the complexity of current research experiments and data demands better tools to enhance productivity. This paper introduces the DAL Toolbox (DALT), a framework designed to address the modern challenges in data analytics workflows. DALT is inspired by Experiment Line concepts and 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 hyperparameter tuning and supports integration with existing libraries and languages. Overall, DALT provides researchers with a comprehensive set of functionalities for data science, promoting ease of use, extensibility, and integration with various tools and libraries.
Page that contains information and links about the article of the same name.
Example of the paper (full version): https://nbviewer.org/github/cefet-rj-dal/daltoolbox-examples/blob/main/timeseries/ts_tune.ipynb
Video presenting the DAL Toolbox Package:
Home Page of DAL Toolbox Package: https://cefet-rj-dal.github.io/daltoolbox/
Soure code of DAL Toolbox Package (GitHub): https://github.com/cefet-rj-dal/daltoolbox
Examples: https://nbviewer.org/github/cefet-rj-dal/daltoolbox-examples/tree/main/