Regression
Showing 36 of 65 projects
An open-source, low-code Python library that automates end-to-end machine learning workflows.
A high-performance gradient boosting library with best-in-class handling of categorical features and support for CPU/GPU training.
A fast and comprehensive machine learning framework for Java, Scala, and Kotlin with state-of-the-art algorithms and data visualization.
A fast, header-only C++ machine learning library with bindings for Python, R, Julia, and Go.
Official code repository for the 'Machine Learning with TensorFlow' book with practical examples.
An open-source numerical library for .NET and Mono providing algorithms for scientific computing, linear algebra, statistics, and more.
A lightweight, dependency-free JavaScript library for descriptive, regression, and inference statistics.
A Python machine learning toolkit for time series analysis with scikit-learn compatible API.
A comprehensive, self-contained mathematics library for PHP with no external dependencies, covering algebra, statistics, linear algebra, and numerical analysis.
A modular active learning framework for Python built on scikit-learn, enabling rapid creation of custom workflows.
A modular active learning framework for Python built on scikit-learn, enabling rapid creation of custom workflows.
A Julia machine learning framework providing a unified interface and meta-algorithms for over 200 models.
A Python library for probabilistic prediction using natural gradient boosting, built on scikit-learn.
A lambda architecture framework on Apache Spark and Kafka for building and deploying real-time large-scale machine learning applications.
A comprehensive Rust library for quantitative finance, offering pricing models, risk analysis, and financial data tools.
A unified interface and infrastructure for machine learning in R, supporting classification, regression, clustering, and survival analysis.
A unified framework for implementing and training deep learning models on tabular data using PyTorch and PyTorch Lightning.
A fast Support Vector Machine (SVM) library that leverages GPUs and multi-core CPUs for high-performance machine learning.
A dedicated OCaml system for scientific and engineering computing, providing n-dimensional arrays, linear algebra, algorithmic differentiation, and neural networks.
A Clojure library for neural networks, regression, and feature learning with GPU acceleration support.
A GPU-accelerated deep learning library for Python using CUDA via PyCUDA, implementing neural networks with various training methods.
A Python library implementing Factorization Machines with a scikit-learn compatible API for regression, classification, and ranking tasks.
A modern, object-oriented machine learning framework for R, providing efficient building blocks for ML workflows.
A fast, ergonomic machine learning library for Rust with broad algorithm coverage and WASM-first defaults.
A Ruby machine learning library with a Scikit-Learn-like interface for classification, regression, clustering, and dimensionality reduction.
MatLab/Octave implementations of popular machine learning algorithms with detailed mathematical explanations and code examples.
A cross-platform C++ machine learning library for real-time gesture recognition with support for classification, regression, and clustering.
A fast implementation of random forests for classification, regression, and survival analysis, optimized for high-dimensional data.
TensorFlow implementation of arbitrary order (≥2) Factorization Machines for classification and regression tasks.
Fast, flexible, multi-threaded ensembles of decision trees for machine learning in pure Go.
A TensorFlow library for training, serving, and interpreting decision forest models like Random Forests and Gradient Boosted Trees.
A Rust crate providing efficient implementations of common machine learning algorithms with support for dense and sparse data.
A Julia package for fitting linear and generalized linear models with comprehensive statistical functionality.
A Go library implementing feedforward/backpropagation neural networks with support for multiple activation functions, solvers, and classification modes.
A Julia package for multivariate statistics and data analysis, including dimension reduction techniques like PCA and LDA.
A framework for building scalable machine learning models in Hadoop using the Scalding DSL.
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