Class ClusteringMetrics (2.1.0)

Stay organized with collections Save and categorize content based on your preferences.
ClusteringMetrics(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Evaluation metrics for clustering models.

Attributes

NameDescription
davies_bouldin_index `.wrappers.DoubleValue`
Davies-Bouldin index.
mean_squared_distance `.wrappers.DoubleValue`
Mean of squared distances between each sample to its cluster centroid.
clusters Sequence[`.gcb_model.Model.ClusteringMetrics.Cluster`]
[Beta] Information for all clusters.

Inheritance

builtins.object > proto.message.Message > ClusteringMetrics

Classes

Cluster

Cluster(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Message containing the information about one cluster.

Methods

__delattr__

__delattr__(key)

Delete the value on the given field.

This is generally equivalent to setting a falsy value.

__eq__

__eq__(other)

Return True if the messages are equal, False otherwise.

__ne__

__ne__(other)

Return True if the messages are unequal, False otherwise.

__setattr__

__setattr__(key, value)

Set the value on the given field.

For well-known protocol buffer types which are marshalled, either the protocol buffer object or the Python equivalent is accepted.