// Licensed to the Apache Software Foundation (ASF) under one or more
// contributor license agreements.  See the NOTICE file distributed with
// this work for additional information regarding copyright ownership.
// The ASF licenses this file to You under the Apache License, Version 2.0
// (the "License"); you may not use this file except in compliance with
// the License.  You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
= Linear SVM (Support Vector Machine)

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.

Apache Ignite Machine Learning module only supports Linear SVM. For more information look at SVM in link:https://en.wikipedia.org/wiki/Support_vector_machine[Wikipedia].

== Model

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


[source, java]
----
SVMLinearClassificationModel model = ...;

double prediction = model.predict(observation);
----

Presently Ignite supports a few parameters for SVMLinearClassificationModel:

* `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)


[source, java]
----
SVMLinearClassificationModel model = ...;

double prediction = model
  .withRawLabels(true)
  .withThreshold(5)
  .predict(observation);
----