How this analysis was created: This summary and feature list were written by an AI model that read the project's README and public documentation pages. Each feature links to the documentation it came from; stars, license and language come straight from the GitHub API. The model does not read the source code, and the analysis is refreshed when the project is re-analysed. Learn more on our About page.
K Means
The main features of reddavis/k-means are: Statistical and Predictive Models.
Open-source alternatives to reddavis/k-means include: davisking/dlib — dlib is a C++ machine learning toolkit and data analysis framework. It provides a collection of algorithms and… ealdent/lda-ruby — This wrapper is based on C-code by David M. Blei. In a nutshell, it can be used to automatically cluster documents… febeling/rb-libsvm — Ruby language bindings for LIBSVM. gbuesing/kmeans-clusterer — KMeansClusterer. igrigorik/decisiontree — ID3-based implementation of the ML Decision Tree algorithm. djcp/linnaeus — Linnaeus is a redis-backed naive Bayesian classification system. Please see the rdoc for more information. Ruby 1.9 is…
dlib is a C++ machine learning toolkit and data analysis framework. It provides a collection of algorithms and utilities for building predictive modeling applications and performing statistical analysis on large datasets within native C++ environments. The project functions as a binding library that wraps low-level C++ machine learning algorithms into high-level Python scripting interfaces. This allows for the integration of high-performance native implementations with Python for machine learning development. The framework covers the implementation of predictive models, the execution of mach
This wrapper is based on C-code by David M. Blei. In a nutshell, it can be used to automatically cluster documents into topics. The number of topics are chosen beforehand and the topics found are usually fairly intuitive. Details of the implementation can be found in the paper by Blei, Ng, and…
Linnaeus is a redis-backed naive Bayesian classification system. Please see the rdoc for more information. Ruby 1.9 is required.
