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.
Model description
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. -
KNNStrategy- could be SIMPLE or WEIGHTED (it enables a weighted k-NN algorithm). -
dataCache- holds a training set of objects for which the class is already known.
// Create trainer
KNNClassificationTrainer trainer = new KNNClassificationTrainer();
// Train model.
KNNClassificationModel knnMdl = trainer.fit(
ignite,
dataCache,
(k, v) -> Arrays.copyOfRange(v, 0, v.length - 1),
(k, v) -> v[2]
)
.withK(3)
.withDistanceMeasure(new EuclideanDistance())
.withStrategy(KNNStrategy.SIMPLE);
// Make a prediction.
double prediction = knnMdl.apply(vectorizedData);Example
An example of kNN Classification is included in the GridGain distribution package.
The training dataset is the Iris dataset which can be loaded from the UCI Machine Learning Repository.
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