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ClusteringMetrics Class

  • Reference

Definition

Namespace:
Microsoft.ML.Data
Assembly:
Microsoft.ML.Data.dll
Package:
Microsoft.ML v3.0.1
Package:
Microsoft.ML v1.0.0
Package:
Microsoft.ML v1.1.0
Package:
Microsoft.ML v1.2.0
Package:
Microsoft.ML v1.3.1
Package:
Microsoft.ML v1.4.0
Package:
Microsoft.ML v1.5.5
Package:
Microsoft.ML v1.6.0
Package:
Microsoft.ML v1.7.0
Package:
Microsoft.ML v2.0.0

The metrics generated after evaluating the clustering predictions.

public sealed class ClusteringMetrics
type ClusteringMetrics = class
Public NotInheritable Class ClusteringMetrics
Inheritance
Object
ClusteringMetrics

Properties

AverageDistance

Average Score. For the K-Means algorithm, the 'score' is the distance from the centroid to the example. The average score is, therefore, a measure of proximity of the examples to cluster centroids. In other words, it is a measure of 'cluster tightness'. Note however, that this metric will only decrease if the number of clusters is increased, and in the extreme case (where each distinct example is its own cluster) it will be equal to zero.
DaviesBouldinIndex

Davies-Bouldin Index is measure of the how much scatter is in the cluster and the cluster separation.
NormalizedMutualInformation

Normalized Mutual Information is a measure of the mutual dependence of the variables. This metric is only calculated if the Label column is provided.

Applies to