A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.
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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.
A collection of research papers on decision, classification and regression trees with implementations.
A minimal benchmark for scalability, speed and accuracy of commonly used open source implementations (R packages, Python scikit-learn, H2O, xgboost, Spark MLlib etc.) of the top machine learning algorithms for binary classification (random forests, gradient boosted trees, deep neural networks etc.).
A curated list of data mining papers about fraud detection.
SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization
A curated list of gradient boosting research papers with implementations.
A collection of state-of-the-art algorithms for the training, serving and interpretation of Decision Forest models in Keras.
The official implementation of "The Shapley Value of Classifiers in Ensemble Games" (CIKM 2021).