You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
The Heuristic Evolutionary Rule Optimization System (HEROS) is a supervised rule-based machine learning algorithm designed to agnostically model diverse 'struct…
scikit-FIBERS (Feature Inclusion Bin Evolver for Risk Stratification) is a scikit-learn compatible machine learning algorithm for modeling or feature learning i…
Source code for the Genetic Architecture Model Emulator for Testing and Evaluating Software (GAMETES) is an algorithm for the generation of complex single nucle…
Feature Inclusion Bin Evolver for Risk Stratification (FIBERS) is an evolutionary algorithm that constructs bins of features, seeking to optimize the bins' stra…
RARE: Relevant Association Rare-variant-bin Evolver (under development); an evolutionary algorithm approach to binning rare variants as a rare variant associati…
Experimental variation of scikit-ExSTraCS that allows the user to import an initial rule population that will get initially evaluated and assigned fitness value…
An automated, rigorous, and largely scikit-learn based machine learning analysis pipeline for binary classification. Adopts current best practices to avoid bias…
A set of Python-based Jupyter notebooks illustrating a documented example of a semi-automated term harmonization pipeline applied to harmonizing medical history…
An (updated and expanded) rigorous, well documented machine learning analysis pipeline for binary classification datasets assembled as a Jupyter Notebook. Inclu…
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=mz…
An rigorous, machine learning analysis pipeline for binary classification datasets assembled as parallelizable command line modules. Includes exploratory analys…
Supplemental materials and code for our GP-LCS project, adapting ExSTraCS to evolve GP trees rather than rules for comparison to other stand-alone GP algorithms
An rigorous, well documented machine learning analysis pipeline for binary classification datasets assembled as a Jupyter Notebook. Includes exploratory analysi…
Code and results for an investigation of pancreatic cancer datasets applying our binary classification machine learning analysis pipeline notebook. Includes an…
This repository includes educational materials on machine learning and a basic example machine learning analysis pipeline. These materials were originally deve…
Assembly of Jupyter notebooks comprising basic machine learning pipeline tasks. This student driven, independent study project will eventually evolve into a us…