docs/_docs/machine-learning/binary-classification/knn-classification.adoc

= k-NN Classification

The Apache Ignite Machine Learning component provides two versions of the widely used k-NN (k-nearest neighbors) algorithm - one for classification tasks and the other for regression tasks.

This documentation reviews k-NN as a solution for classification tasks.

== Trainer and Model

The k-NN algorithm is a non-parametric method whose input consists of the k-closest training examples in the feature space.

Also, k-NN classification's output represents a class membership. An object is classified by the majority votes of its neighbors. The object is assigned to a particular class that is most common among its k nearest neighbors. `k` is a positive integer, typically small. There is a special case when `k` is `1`, then the object is simply assigned to the class of that single nearest neighbor.

Presently, Ignite supports a few parameters for k-NN classification algorithm:

* `k` - a number of nearest neighbors
* `distanceMeasure` - one of the distance metrics provided by the ML framework such as Euclidean, Hamming or Manhattan.
* `isWeighted` - false by default, if true it enables a weighted KNN algorithm.
* `dataCache` -  holds a training set of objects for which the class is already known.
* `indexType` - distributed spatial index, has three values: ARRAY, KD_TREE, BALL_TREE.


[source, java]
----
// Create trainer
KNNClassificationTrainer trainer = new KNNClassificationTrainer();

// Create trainer
KNNClassificationTrainer trainer = new KNNClassificationTrainer()
  .withK(3)
  .withIdxType(SpatialIndexType.BALL_TREE)
  .withDistanceMeasure(new EuclideanDistance())
  .withWeighted(true);

// Train model.
KNNClassificationModel knnMdl = trainer.fit(
  ignite,
  dataCache,
  vectorizer
);

// Make a prediction.
double prediction = knnMdl.predict(observation);
----

== Example

To see how kNN Classification can be used in practice, try this https://github.com/apache/ignite/blob/master/examples/src/main/java/org/apache/ignite/examples/ml/knn/KNNClassificationExample.java[example] that is available on GitHub and delivered with every Apache Ignite distribution.

The training dataset is the Iris dataset which can be loaded from the https://archive.ics.uci.edu/ml/datasets/iris[UCI Machine Learning Repository].