Showing 18 of 18 projects
A fast, distributed gradient boosting framework based on decision tree algorithms for ranking, classification, and other ML tasks.
A fast, distributed gradient boosting framework based on decision tree algorithms for ranking, classification, and other machine learning tasks.
An open-source, low-code Python library that automates end-to-end machine learning workflows.
An open-source library for building massively scalable machine learning pipelines on Apache Spark.
Transpile trained machine learning models into native code (Java, C, Python, Go, etc.) with zero dependencies.
A toolkit for distributed machine learning featuring parameter server framework, topic modeling, gradient boosting, and word embedding.
Automated machine learning library for production and analytics, handling feature engineering, model selection, and hyperparameter optimization.
A Python library for time series forecasting using scikit-learn compatible machine learning models.
A Python library for time series forecasting using scikit-learn compatible machine learning models.
A Python framework for scalable time series forecasting using machine learning models, designed for production environments.
An open dataset and toolkit for training static PE malware machine learning models, featuring millions of labeled Windows executable samples.
An open dataset and toolkit for training static PE malware machine learning models, featuring extracted features from millions of Windows executable files.
A curated collection of gradient boosting research papers with implementations from top machine learning conferences.
A Ruby library for building and serving predictive models with support for PMML and integration with Python and R models.
A pure Go library for making predictions with Gradient Boosting Regression Trees models from LightGBM, XGBoost, and scikit-learn.
An open-source machine learning solution for the Home Credit Default Risk Kaggle competition, providing reproducible code and experiments.
A benchmark dataset with 3.2 million malicious and benign files across 6 file types for evaluating malware classifiers.
A Ruby gem providing high-performance gradient boosting with LightGBM for machine learning tasks.
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