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SVM Binary Classification

Support Vector Machines (SVMs) are supervised learning models with associated learning algorithms that analyze data used for classification and regression analysis.

Given a set of training examples, each marked as belonging to one or the other of two categories, an SVM training algorithm builds a model that assigns new examples to one category or the other, making it a non-probabilistic binary linear classifier.

Only Linear SVM is supported in the Apache Ignite Machine Learning module. For more information look at SVM in Wikipedia.


A Model in the case of SVM is represented by the class SVMLinearBinaryClassificationModel. It enables a prediction to be made for a given vector of features, in the following way:

SVMLinearBinaryClassificationModel model = ...;

double prediction = model.predict(observation);

Presently Ignite supports the following parameters for SVMLinearBinaryClassificationModel:

  • isKeepingRawLabels - controls the output label format: -1 and +1 for false value and raw distances from the separating hyperplane (default value: false)

  • threshold - a threshold to assign +1 label to the observation if the raw value is more than this threshold (default value: 0.0)

SVMLinearBinaryClassificationModel model = ...;

double prediction = model


Base class for a soft-margin SVM linear classification trainer based on the communication-efficient distributed dual coordinate ascent algorithm (CoCoA) with hinge-loss function. This trainer takes input as a Labeled Dataset with -1 and +1 labels for two classes and makes binary classification.

The paper about this algorithm could be found here

Presently, Ignite supports the following parameters for SVMLinearBinaryClassificationTrainer:

  • amountOfIterations - amount of outer SDCA algorithm iterations. (default value: 200)

  • amountOfLocIterations - amount of local SDCA algorithm iterations. (default value: 100)

  • lambda - regularization parameter (default value: 0.4)

// Set up the trainer
SVMLinearBinaryClassificationTrainer trainer = new SVMLinearBinaryClassificationTrainer()

// Build the model
SVMLinearBinaryClassificationModel mdl =


To see how SVM Linear Classifier can be used in practice, try this example.

The training dataset is the subset of the Iris dataset (classes with labels 1 and 2, which are presented linear separable two-classes dataset) which could be loaded from the UCI Machine Learning Repository.