h2o
H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.
https://github.com/pycroscopy/atomai
Deep and Machine Learning for Microscopy
https://github.com/salesforce/merlion
Merlion: A Machine Learning Framework for Time Series Intelligence
MLJ
MLJ: A Julia package for composable machine learning - Published in JOSS (2020)
automlpipeline.jl
A package that makes it trivial to create and evaluate machine learning pipeline architectures.
pynets
A Reproducible Workflow for Structural and Functional Connectome Ensemble Learning
arboreto
A scalable python-based framework for gene regulatory network inference using tree-based ensemble regressors.
deepbrainseg
Fully automatic brain tumour segmentation using Deep 3-D convolutional neural networks
https://github.com/business-science/modeltime.ensemble
Time Series Ensemble Forecasting
drtmle
Nonparametric estimators of the average treatment effect with doubly-robust confidence intervals and hypothesis tests
https://github.com/robert-forrest/cerebral
Tool for creating multi-output deep ensemble neural-networks
repic
REliable PIcking by Consensus (REPIC) - an ensemble learning methodology for cryo-EM particle picking
https://github.com/amr-yasser226/intrusion-detection-kaggle
End-to-end pipeline for multi-class cyber-attack detection using per-flow network features: data profiling, deduplication, skew-correction, outlier treatment, feature engineering, imbalance handling, and tree-based modeling (XGBoost, LightGBM, CatBoost, stacking), with a final Kaggle submission scoring 0.9146 public / 0.9163 private.
cxrabnormalitylocalization
🫁 Chest X-ray abnormalities localization via ensemble of deep convolutional neural networks