SVMKit is a machine learninig library in Ruby
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SVMKit is a machine learninig library in Ruby. SVMKit provides machine learning algorithms with interfaces similar to Scikit-Learn in Python. SVMKit currently supports Linear / Kernel Support Vector Machine, Logistic Regression, Linear Regression, Ridge, Lasso, Factorization Machine, Naive Bayes, Decision Tree, AdaBoost, Random Forest, K-nearest neighbor classifier, K-Means, DBSCAN, Principal Component Analysis, Non-negative Matrix Factorization and cross-validation.


Add this line to your application's Gemfile:

gem 'svmkit'

And then execute:

$ bundle

Or install it yourself as:

$ gem install svmkit


Example 1. Pendigits dataset classification

SVMKit provides function loading libsvm format dataset file. We start by downloading the pendigits dataset from LIBSVM Data web site.

$ wget
$ wget

Training of the classifier with Linear SVM and RBF kernel feature map is the following code.

require 'svmkit'

# Load the training dataset.
samples, labels = SVMKit::Dataset.load_libsvm_file('pendigits')

# If the features consists only of integers, load_libsvm_file method reads in Numo::Int32 format.
# As necessary, you should convert sample array to Numo::DFloat format.
samples = Numo::DFloat.cast(samples)

# Map training data to RBF kernel feature space.
transformer = 0.0001, n_components: 1024, random_seed: 1)
transformed = transformer.fit_transform(samples)

# Train linear SVM classifier.
classifier = 0.0001, max_iter: 1000, batch_size: 50, random_seed: 1), labels)

# Save the model.'transformer.dat', 'wb') { |f| f.write(Marshal.dump(transformer)) }'classifier.dat', 'wb') { |f| f.write(Marshal.dump(classifier)) }

Classifying testing data with the trained classifier is the following code.

require 'svmkit'

# Load the testing dataset.
samples, labels = SVMKit::Dataset.load_libsvm_file('pendigits.t')
samples = Numo::DFloat.cast(samples)

# Load the model.
transformer = Marshal.load(File.binread('transformer.dat'))
classifier = Marshal.load(File.binread('classifier.dat'))

# Map testing data to RBF kernel feature space.
transformed = transformer.transform(samples)

# Classify the testing data and evaluate prediction results.
puts("Accuracy: %.1f%%" % (100.0 * classifier.score(transformed, labels)))

# Other evaluating approach
# results = classifier.predict(transformed)
# evaluator =
# puts("Accuracy: %.1f%%" % (100.0 * evaluator.score(results, labels)))

Execution of the above scripts result in the following.

$ ruby train.rb
$ ruby test.rb
Accuracy: 98.4%

Example 2. Cross-validation

require 'svmkit'

# Load dataset.
samples, labels = SVMKit::Dataset.load_libsvm_file('pendigits')
samples = Numo::DFloat.cast(samples)

# Define the estimator to be evaluated.
lr = 0.0001, random_seed: 1)

# Define the evaluation measure, splitting strategy, and cross validation.
ev =
kf = 5, shuffle: true, random_seed: 1)
cv = lr, splitter: kf, evaluator: ev)

# Perform 5-cross validation.
report = cv.perform(samples, labels)

# Output result.
mean_logloss = report[:test_score].inject(:+) / kf.n_splits
puts("5-CV mean log-loss: %.3f" % mean_logloss)


After checking out the repo, run bin/setup to install dependencies. Then, run rake spec to run the tests. You can also run bin/console for an interactive prompt that will allow you to experiment.

To install this gem onto your local machine, run bundle exec rake install. To release a new version, update the version number in version.rb, and then run bundle exec rake release, which will create a git tag for the version, push git commits and tags, and push the .gem file to


Bug reports and pull requests are welcome on GitHub at This project is intended to be a safe, welcoming space for collaboration, and contributors are expected to adhere to the Contributor Covenant code of conduct.


The gem is available as open source under the terms of the BSD 2-clause License.

Code of Conduct

Everyone interacting in the SVMKit project’s codebases, issue trackers, chat rooms and mailing lists is expected to follow the code of conduct.