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

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 NegLog2R

SUBMITTER: Nasimian A 

PROVIDER: S-EPMC10801203 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature