Regression
Showing 36 of 76 projects
A scikit-learn compatible Python library for probabilistic regression, survival analysis, and probability distributions.
A Julia interface for XGBoost, providing efficient distributed gradient boosting for regression, classification, and ranking.
A Node.js library implementing Support Vector Machines (SVM) for classification and regression tasks.
Simple machine learning library / 簡單易用的機器學習套件
Ruby language bindings for the LIBSVM library, enabling support vector machine (SVM) classification and regression in Ruby.
A Golden Master-based test framework for Selenium that enables deep visual and functional regression testing with unbreakable element identification.
A web interface and REST API for classification and regression using Support Vector Machine (SVM) and Support Vector Regression (SVR) algorithms.
A high-performance, type-safe DataFrame library for the JVM enabling large-scale data analysis with parallel processing capabilities.
An idiomatic Clojure machine learning library providing a unified interface for classification, regression, and unsupervised models.
A scikit-learn compatible Python implementation of the Relevance Vector Machine for sparse Bayesian learning.
A high-performance visual regression testing tool that catches UI regressions with fast image comparisons.
A Node.js library implementing Decision Tree (ID3/CART), Random Forest, and XGBoost algorithms with TypeScript support and automatic data type detection.
A Scala and JVM machine learning toolbox for research, education, and industry with an interactive REPL and end-to-end pipelines.
A simple machine learning framework written in Swift, currently focusing on regression algorithms.
A machine learning library for Clojure built on top of Weka, providing filters, classifiers, regression, and clustering algorithms.
A lightweight feedforward neural network with resilient backpropagation (Rprop), implemented in pure Ruby with no external dependencies.
A Ruby interface to XGBoost, providing high-performance gradient boosting for machine learning tasks.
Converts R regression model outputs into publication-ready LaTeX or HTML tables for easy model comparison.
A machine learning and optimization framework for Objective-C and Swift, focused on regression and multi-objective evolutionary algorithms.
A parallel Random Forest implementation in Go for classification and regression tasks.
A simple and functional machine learning library for Erlang, Elixir, and Gleam projects.
A PHP library for building predictions using linear regression with simple data fitting.
A Julia wrapper for fitting Lasso and ElasticNet GLM models using the glmnet Fortran library.
A comprehensive Ruby suite for performing basic and advanced statistical analysis, including regression, factor analysis, and reliability testing.
A Ruby gem providing high-performance gradient boosting with LightGBM for machine learning tasks.
A Go module implementing multi-layer neural networks for machine learning tasks.
Ruby interface to LIBLINEAR for machine learning classification and regression tasks using SWIG bindings.
A Go port of LIBSVM 3.14, providing support vector machine (SVM) algorithms for classification and regression.
A fast and versatile implementation of support vector machines with integrated hyper-parameter selection and support for multiple learning scenarios.
A Clojure library providing machine learning algorithms with simple APIs for data preprocessing and modeling.
Random forest in Common Lisp
Rust bindings for LightGBM, enabling gradient boosting for machine learning tasks in Rust.
An open-source machine learning library for regression tasks using the Relevance Vector Machine (RVM) technique.
A flexible AutoML library for Python that automates model selection and hyperparameter tuning for regression and classification tasks.
A Go library using Cgo for blazing fast inference of CatBoost gradient boosting models.
A Swift library for building predictions using linear regression with simple API and statistical insights.
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