Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow
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Transform ML models into a native code (Java, C, Python, Go, JavaScript, Visual Basic, C#, R, PowerShell, PHP, Dart, Haskell, Ruby, F#, Rust) with zero dependencies
A library for debugging/inspecting machine learning classifiers and explaining their predictions
A collection of research papers on decision, classification and regression trees with implementations.
Automatically Visualize any dataset, any size with a single line of code. Created by Ram Seshadri. Collaborators Welcome. Permission Granted upon Request.
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.).
Python library for time series forecasting using scikit-learn compatible models, statistical methods, and foundation models
MLBox is a powerful Automated Machine Learning python library.
Scalable machine 🤖 learning for time series forecasting.
A curated list of gradient boosting research papers with implementations.
A fast xgboost feature selection algorithm
Stacked Generalization (Ensemble Learning)