Experimental

org.apache.spark.ml.clustering

Class KMeansSummary

Object

org.apache.spark.ml.clustering.ClusteringSummary

org.apache.spark.ml.clustering.KMeansSummary

All Implemented Interfaces:

java.io.Serializable

public class KMeansSummary
extends ClusteringSummary

Summary of KMeans.

param: predictions DataFrame produced by KMeansModel.transform(). param: predictionCol Name for column of predicted clusters in predictions. param: featuresCol Name for column of features in predictions. param: k Number of clusters. param: numIter Number of iterations. param: trainingCost K-means cost (sum of squared distances to the nearest centroid for all points in the training dataset). This is equivalent to sklearn's inertia.

See Also:

Serialized Form

Method Summary

Modifier and Type	Method and Description
double	trainingCost()

Methods inherited from class org.apache.spark.ml.clustering.ClusteringSummary

cluster, clusterSizes, featuresCol, k, numIter, predictionCol, predictions

Methods inherited from class Object

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

Method Detail

trainingCost

public double trainingCost()