Practical overview
When it fits
Use Rumale when machine-learning functionality needs to live in a Ruby application or Ruby-oriented data workflow and the documented API covers the algorithms and evaluation stages required.
Who it is for
- Ruby developers integrating machine learning into applications
- Teams creating Ruby-based data-analysis tools
- Data scientists prototyping workflows in the Ruby ecosystem
- Developers building reusable Ruby machine-learning components
Documented uses
- Train classification and regression models
- Load LIBSVM-format datasets
- Transform features and reduce data dimensions
- Evaluate models with scoring and cross-validation
- Serialize trained model components for later use
Requirements & limits
The project is available through a public GitHub repository and RubyGems references. The README identifies the BSD 3-Clause License.
- Ruby is the implementation language and primary ecosystem.
- The API is designed with interfaces similar to Scikit-Learn.
- The library covers supervised learning, clustering, dimensionality reduction, feature transformation and evaluation.
- Optional Numo::Linalg Alternative and Parallel integrations address selected numerical and parallel workloads.
- The documented installation paths use a Gemfile with Bundler or the
gem install rumalecommand.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Install Rumale for a Ruby machine-learning project
Add the Rumale gem to a Ruby application and load its library API.
Before you start
- A Ruby development environment.
- A Ruby application using a Gemfile or access to the gem install command.
Steps
Add
gem 'rumale'to the application's Gemfile and runbundle, or install the gem withgem install rumale.Load Rumale in a Ruby script with
require 'rumale'.Use the Rumale API documentation to select algorithms and workflow components.
Expected outcome
Rumale is installed and available to the Ruby application through its documented API.
Guide limitations
- This recipe covers installation and loading only; it does not establish that a particular model or dataset workflow will run in a given environment.
Train and evaluate a documented LIBSVM classification example
Follow the README workflow for loading a LIBSVM dataset, training an SVM classifier and evaluating test predictions.
Before you start
- Rumale installed in Ruby.
- The
pendigitsandpendigits.tdatasets downloaded from the LIBSVM Data site. - A Ruby script environment.
Steps
Load the training dataset with
Rumale::Dataset.load_libsvm_file.Transform the samples with
Rumale::KernelApproximation::RBFand fitRumale::LinearModel::SVC.Serialize the transformer and classifier with Ruby Marshal.
Load the test dataset and serialized objects, transform the samples, and score or predict the test data.
Expected outcome
The documented example produces an accuracy result for the pendigits test data; the README reports 98.5% for its example execution.
Guide limitations
- The reported accuracy belongs to the README's example and dataset workflow, not a general performance guarantee.
- The recipe depends on the named external datasets and the versions and environment used by the project example.
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 3 October 2026
- Original website title
- yoshoku/rumale · Official homepage title · retained in its source language