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scikit-FIBERS
Publicscikit-FIBERS (Feature Inclusion Bin Evolver for Risk Stratification) is a scikit-learn compatible machine learning algorithm for modeling or feature learning in survival analyses where feature 'burden' may be predictive of risk strata. Originally designed to identify amino-acid positions where mismatch burden predicts kidney graft failure risk.scikit-rebate
PublicSTREAMLINE
PublicSimple Transparent End-To-End Automated Machine Learning Pipeline for Supervised Learning in Tabular Binary Classification Datascikit-ExSTraCS
Publicscikit-eLCS
Publicscikit-XCS
PublicGAMETES
Publicsurvival-LCS
PublicI2C2-Documentation
PublicFIBERS
Publicscikit-RARE
PublicRARE
PublicRARE: Relevant Association Rare-variant-bin Evolver (under development); an evolutionary algorithm approach to binning rare variants as a rare variant association analysis tool. Applications, visualizations, and modifications currently in works.scikit-ExSTraCS-RuleInit
PublicExperimental variation of scikit-ExSTraCS that allows the user to import an initial rule population that will get initially evaluated and assigned fitness values prior to the start of learning iterations. This allows for the import of manually curated expert knowledge derived rules, or rules derived from other sources.AutoMLPipe-BC
PublicAn 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) da…auto_term_harm_pipe
PublicA set of Python-based Jupyter notebooks illustrating a documented example of a semi-automated term harmonization pipeline applied to harmonizing medical history terms across 28 clinical trials of pulminary arterial hypertension- An (updated and expanded) rigorous, well documented machine learning analysis pipeline for binary classification datasets assembled as a Jupyter Notebook. Includes exploratory analysis, data processing, feature processing, ML modeling (13 algorithms) with hyperparameter sweeps, visualizations, and statistical analysis. A comprehensive starting p…
- Example PyKE code and Jupyter Notebook for a simple backwards chaining expert system as described in this lecture on YouTube: https://www.youtube.com/watch?v=mzsk5_EmZq8
- An rigorous, machine learning analysis pipeline for binary classification datasets assembled as parallelizable command line modules. Includes exploratory analysis, data processing, feature processing, ML modeling (11 algorithms) with hyperparameter sweeps, visualizations, and statistical analysis. A comprehensive starting point to adapt to your …
gametes_archive_gen
PublicGP-LCS
Public- An rigorous, well documented machine learning analysis pipeline for binary classification datasets assembled as a Jupyter Notebook. Includes exploratory analysis, data processing, feature processing, ML modeling (9 algorithms, including the original ExSTraCS algorithm) with hyperparameter sweeps, visualizations, and statistical analysis. A compr…
- Code and results for an investigation of pancreatic cancer datasets applying our binary classification machine learning analysis pipeline notebook. Includes analysis and comparison of three pancreatic cancer datasets.
ML_Pipeline_Notebooks
Publicindependent-study-18fall
PublicAssembly of Jupyter notebooks comprising basic machine learning pipeline tasks. This student driven, independent study project will eventually evolve into a user-friendly starting point for ML pipeline example notebooks.