AlphaML: A Clear, Legible, Explainable, Transparent, and Elucidative Binary Classification Platform for Tabular Data

Overview

Journal Patterns (N Y)

Date 2024 Jan 24

PMID 38264719

Authors

Ahmad Nasimian

Saleena Younus

Ozge Tatli

Emma U Hammarlund

Kenneth J Pienta

Lars Ronnstrand

Julhash U Kazi

Affiliations

Soon will be listed here.

Abstract

Leveraging the potential of machine learning and recognizing the broad applications of binary classification, it becomes essential to develop platforms that are not only powerful but also transparent, interpretable, and user friendly. We introduce alphaML, a user-friendly platform that provides clear, legible, explainable, transparent, and elucidative (CLETE) binary classification models with comprehensive customization options. AlphaML offers feature selection, hyperparameter search, sampling, and normalization methods, along with 15 machine learning algorithms with global and local interpretation. We have integrated a custom metric for hyperparameter search that considers both training and validation scores, safeguarding against under- or overfitting. Additionally, we employ the NegLog2RMSL scoring method, which uses both training and test scores for a thorough model evaluation. The platform has been tested using datasets from multiple domains and offers a graphical interface, removing the need for programming expertise. Consequently, alphaML exhibits versatility, demonstrating promising applicability across a broad spectrum of tabular data configurations.

Citing Articles

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AlphaML: A clear, legible, explainable, transparent, and elucidative binary classification platform for tabular data.

Nasimian A, Younus S, Tatli O, Hammarlund E, Pienta K, Ronnstrand L Patterns (N Y). 2024; 5(1):100897.

PMID: 38264719 PMC: 10801203. DOI: 10.1016/j.patter.2023.100897.

PLK1 as a cooperating partner for BCL2-mediated antiapoptotic program in leukemia.

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