Popular GitHub Repositories for Boosting

A game theoretic approach to explain the output of any machine learning model.

A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.

Machine learning, in numpy

A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.

A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.

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.

Fit interpretable models. Explain blackbox machine learning.

?? Lobe theme - The modern theme for stable diffusion webui, exquisite interface design, highly customizable UI, and efficiency boosting features.

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.).

Text Classification Algorithms: A Survey

Natural Gradient Boosting for Probabilistic Prediction

[UNMAINTAINED] Automated machine learning for analytics & production

Boosting your Web Services of Deep Learning Applications.

A unified ensemble framework for PyTorch to improve the performance and robustness of your deep learning model.

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.

Examples of techniques for training interpretable ML models, explaining ML models, and debugging ML models for accuracy, discrimination, and security.

NBoost is a scalable, search-api-boosting platform for deploying transformer models to improve the relevance of search results on different platforms (i.e. Elasticsearch)

Building and training Speech Emotion Recognizer that predicts human emotions using Python, Sci-kit learn and Keras