k-NN Regression
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 the regression 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. Each training example has a property value in a numerical form associated with the given training example.
The k-NN algorithm uses all training sets to predict a property value for the given test sample.
This predicted property value is an average of the values of its k nearest neighbors. If k is 1, then the test sample is simply assigned to the property value of a single nearest neighbor.
Presently, Ignite supports a few parameters for the k-NN regression 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), -
datasetBuilder- helps to get access to the training set of objects for which the class is already known.
// Create trainer
KNNRegressionTrainer trainer = new KNNRegressionTrainer();
// Train model.
KNNRegressionModel knnMdl = (KNNRegressionModel) trainer.fit(
datasetBuilder,
(k, v) -> Arrays.copyOfRange(v, 1, v.length),
(k, v) -> v[0])
.withK(5)
.withDistanceMeasure(new ManhattanDistance())
.withStrategy(KNNStrategy.WEIGHTED);
// Make a prediction.
double prediction = knnMdl.apply(vectorizedData);Example
An example of the k-NN regression is included in the GridGain distribution package.
The training dataset is the Computer Hardware Data Set which can be loaded from the UCI Machine Learning Repository.
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