Classification
Showing 36 of 74 projects
A modern C++ toolkit for text retrieval and analysis, featuring indexing, ranking, topic modeling, classification, and language models.
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 Go library implementing feedforward/backpropagation neural networks with support for multiple activation functions, solvers, and classification modes.
A Spark Streaming library for mining big data streams with incremental learning algorithms.
A scikit-learn compatible classifier that produces human-interpretable decision rules instead of black box models.
A Python library for class-imbalanced ensemble learning with 30+ algorithms, built on scikit-learn.
A Scala framework for distributed supervised learning of decision tree ensemble models, inspired by Google's PLANET.
A tool for automatic analysis of malware behavior using machine learning to identify, cluster, and classify malicious software.
A framework for building scalable machine learning models in Hadoop using the Scalding DSL.
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.
Ruby language bindings for the LIBSVM library, enabling support vector machine (SVM) classification and regression in Ruby.
A high-performance, large-scale statistical machine learning library written in Common Lisp.
A web interface and REST API for classification and regression using Support Vector Machine (SVM) and Support Vector Regression (SVR) algorithms.
FLAME dataset and deep learning models for fire detection in aerial imagery using UAVs, supporting classification and segmentation tasks.
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 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.
Automatically classifies and labels urban point clouds using data fusion with public datasets and region growing techniques.
A Swiss knife collection of utility functions for developing and evaluating machine learning algorithms in Julia.
A genetic programming platform for Python with TensorFlow for fast CPU and GPU symbolic regression and classification.
Machine Learning and Digital Signal Processing library for MicroPython, enabling TinyML on microcontrollers without C code.
A public dataset of field images with segmentation masks and plant type annotations for computer vision in precision agriculture.
A Clojure wrapper for the Encog machine learning framework, specializing in neural network construction and training.
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.
A Julia library providing a consistent API for common machine learning algorithms, designed for practitioners working with in-memory datasets.
A simple and functional machine learning library for Erlang, Elixir, and Gleam projects.
A parallel Random Forest implementation in Go for classification and regression tasks.
A Scalding library for machine learning and statistical analysis, featuring Mahout vector integration, K-Means clustering, and Naive-Bayes classifiers.
A Julia wrapper for fitting Lasso and ElasticNet GLM models using the glmnet Fortran library.
A Go library for scoring machine learning models using PMML, supporting neural networks, decision trees, random forests, and gradient boosted models.
A Ruby gem providing high-performance gradient boosting with LightGBM for machine learning tasks.
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