Classification
Showing 31 of 103 projects
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 fungal image classification project using ResNet to identify mushroom species from citizen science and expert sources.
A Ruby gem for scoring predictive models using PMML, supporting decision trees, naive Bayes, logistic regression, random forests, and gradient boosted trees.
A pattern recognition library for Go providing classification, clustering, and feature extraction algorithms.
A Clojure library providing machine learning algorithms with simple APIs for data preprocessing and modeling.
A JRuby gem providing Ruby interfaces for Weka's machine learning and data mining algorithms.
A JavaScript library implementing logistic regression and C4.5 decision tree algorithms for machine learning in the browser and Node.js.
Rust bindings for LightGBM, enabling gradient boosting for machine learning tasks in Rust.
A JavaScript implementation of the k-nearest neighbors algorithm for supervised machine learning.
Torch7 library providing SVM implementations including SGD-based methods and LIBLINEAR wrapper.
A Go library for building and evaluating dynamic decision trees programmatically or from JSON, with support for pre-processing inputs.
Native Julia implementations of standard SVM algorithms, including Pegasos and Dual Coordinate Descent.
A Scala machine learning library with simple, readable implementations of classic algorithms for prototyping and education.
A Python implementation of the Optimum-Path Forest classifier for machine learning tasks.
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.
An open-source JavaScript library implementing machine learning algorithms for educational purposes with interactive visualizations.
A Julia package for regularized linear and quadratic discriminant analysis (LDA/QDA) classification.
A Julia package implementing Decision Tree (CART) and Random Forest algorithms for classification and regression tasks.
Raku bindings for libsvm, providing support vector machine algorithms for classification, regression, and outlier detection.
A simple implementation of Multinomial Naive Bayes classification in Julia.
A simple Java library implementing the K-Nearest Neighbor algorithm for supervised learning with multiple similarity metrics.
A Go package providing error handling with classification primitives for simple and detailed errors.
A fast Single Layer Perceptron library for Deno, written in Rust and TypeScript with Foreign Function Interface (FFI).
Python package for streamlined training, evaluation, and prediction of ML models on cleaned datasets.
Compares stacked ensemble learning against individual ML algorithms for stock market trend prediction using Python.
A machine learning project that predicts heart disease risk using various classification models on clinical data.
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