UrbsLab/AutoMLPipe-BC
An automated, rigorous, and largely scikit-learn based machine learning analysis pipeline for binary classification. Adopts current best practices to avoid bias, optimize performance, ensure replicatability, capture complex associations (e.g. interactions and heterogeneity), and enhance interpretability. Includes (1) exploratory analysis, (2) data cleaning, (3) partitioning, (4) scaling, (5) imputation, (6) filter-based feature selection, (7) collective feature selection, (8) modeling with 'optuna' hyperparameter optimization across 13 implemented ML algorithms (including three rule-based machine learning algorithms: ExSTraCS, XCS, and eLCS), (9) testing evaluations with 16 classification metrics, model feature importance estimation, (10) automatically saves all results, models, and publication-ready plots (including proposed composite feature importance plots), (11) non-parametric statistical comparisons across ML algorithms and analyzed datasets, and (12) automatically generated PDF summary reports.
Jupyter NotebookGPL-3.0