Projects in Awesome Lists by UrbsLab

https://github.com/urbslab/streamline

Simple Transparent End-To-End Automated Machine Learning Pipeline for Supervised Learning in Tabular Binary Classification Data

automl-pipeline binary-classification data-science data-visualization feature-selection imputation machine-learning model-application statistical-analysis supervised-learning

Last synced: 12 Jul 2025

https://github.com/urbslab/pyke_expertsystem_example_bmin520

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

Last synced: 07 Oct 2025

https://github.com/urbslab/scikit-elcs

A scikit-learn-compatible Python implementation of eLCS, a supervised learning variant of Learning Classifier Systems

Last synced: 12 Jul 2025

https://github.com/urbslab/scikit-xcs

scikit learn compatible implementation of XCS, the most popular and best studied learning classifier system algorithm to date.

Last synced: 12 Jul 2025

https://github.com/urbslab/exstracs_ml_pipeline_binary_notebook

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 comprehensive starting point to adapt to your own dataset an as an example of how to integrate a non-scikit-learn ML algorithm into a comparative pipeline.

Last synced: 12 Jul 2025

https://github.com/urbslab/scikit_ml_pipeline_binary_notebook

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 point to adapt to your own dataset.

Last synced: 12 Jul 2025

https://github.com/urbslab/ml_pipeline_notebooks

This repository includes educational materials on machine learning and a basic example machine learning analysis pipeline. These materials were originally developed for a workshop series at the University of Pennsylvania.

Last synced: 12 Jul 2025

https://github.com/urbslab/scikit-fibers

scikit-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.

Last synced: 12 Jul 2025

https://github.com/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.

Last synced: 12 Jul 2025

https://github.com/urbslab/gametes

Source code for the Genetic Architecture Model Emulator for Testing and Evaluating Software (GAMETES) is an algorithm for the generation of complex single nucleotide polymorphism (SNP) models for simulated association studies.

Last synced: 12 Jul 2025

https://github.com/urbslab/lcs-visualization-pipeline

LCS Discovery and Visualization Environment (LCS-DIVE)

Last synced: 12 Jul 2025

https://github.com/urbslab/pancreatic_cancer_ml_notebook_analysis

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.

Last synced: 23 Oct 2025

https://github.com/urbslab/scikit-exstracs-ruleinit

Experimental 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.

Last synced: 12 Jul 2025

https://github.com/urbslab/auto_term_harm_pipe

A 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

Last synced: 12 Jul 2025

https://github.com/urbslab/scikit_ml_pipeline_binary_parallel

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 own dataset.

Last synced: 12 Jul 2025

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