com.workfusion.vds.sdk.ml.sklearn.kneighbors

Class KNeighborsClassifier

java.lang.Object

com.workfusion.vds.sdk.ml.python.BasePythonClassifier

com.workfusion.vds.sdk.ml.sklearn.kneighbors.KNeighborsClassifier

All Implemented Interfaces:

Classifier, PythonClassifier, AutoCloseable

public class KNeighborsClassifier
extends BasePythonClassifier

The type KNeighbor classifier.

Since:

10.0

Author:

Konstantin Kovalev

See Also:

KNeighborsClassifier

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	KNeighborsClassifier.Builder The type Builder.

Method Summary

Modifier and Type	Method and Description
static KNeighborsClassifier.Builder	builder() Construct new builder instance.
AlgorithmType	getAlgorithm() Gets algorithm used to compute the nearest neighbors.
DistanceMetricType	getDistanceMetric() Gets the distance metric to use for the tree.
int	getJobs() Gets the number of jobs to run in parallel for both fit and predict.
int	getLeafSize() Gets the leaf size.
int	getNeighbors() Gets number of neighbors to use by default for kneighbors queries.
int	getPower() Gets the power parameter for the Minkowski metric.
WeightFunctionType	getWeightFunction() Gets the weight function used in prediction.

Methods inherited from class com.workfusion.vds.sdk.ml.python.BasePythonClassifier

close, fit, getModelName, getSerializableModel, getWorkingDir, init, predict

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Method Detail

getNeighbors

public int getNeighbors()

Gets number of neighbors to use by default for kneighbors queries.

Returns:

the neighbors

getWeightFunction

public WeightFunctionType getWeightFunction()

Gets the weight function used in prediction.

Returns:

the weight function

getAlgorithm

public AlgorithmType getAlgorithm()

Gets algorithm used to compute the nearest neighbors.

Returns:

the algorithm

getLeafSize

public int getLeafSize()

Gets the leaf size. This can affect the speed of the construction and query, as well as the memory required to store the tree. The optimal value depends on the nature of the problem.

Returns:

the leaf size

getDistanceMetric

public DistanceMetricType getDistanceMetric()

Gets the distance metric to use for the tree.

Returns:

the distance metric

getPower

public int getPower()

Gets the power parameter for the Minkowski metric.

Returns:

the power parameter

getJobs

public int getJobs()

Gets the number of jobs to run in parallel for both fit and predict. If -1, then the number of jobs is set to the number of cores.

Returns:

the jobs

builder

public static KNeighborsClassifier.Builder builder()

Construct new builder instance.

Returns:

new builder instance