Classification
Showing 36 of 103 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.
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 high-performance, large-scale statistical machine learning library written in Common Lisp.
FLAME dataset and deep learning models for fire detection in aerial imagery using UAVs, supporting classification and segmentation tasks.
A web interface and REST API for classification and regression using Support Vector Machine (SVM) and Support Vector Regression (SVR) algorithms.
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.
Machine Learning and Digital Signal Processing library for MicroPython, enabling TinyML on microcontrollers without C code.
A genetic programming platform for Python with TensorFlow for fast CPU and GPU symbolic regression and classification.
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 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 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.
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