HomogeneityScore

Assesses clustering homogeneity by comparing true and predicted labels, scoring from 0 (heterogeneous) to 1 (homogeneous).

Purpose

The Homogeneity Score encapsulated in this performance test is used to measure the homogeneity of the clusters formed by a machine learning model. In simple terms, a clustering result satisfies homogeneity if all of its clusters contain only points which are members of a single class.

Test Mechanism

This test uses the homogeneity_score function from the sklearn.metrics library to compare the ground truth class labels of the training and testing sets with the labels predicted by the given model. The returned score is a metric of the clustering accuracy, and ranges from 0.0 to 1.0, with 1.0 denoting the highest possible degree of homogeneity.

Signs of High Risk

• A score close to 0: This denotes that clusters are highly heterogenous and points within the same cluster might not belong to the same class.
• A significantly lower score for testing data compared to the score for training data: This can indicate overfitting, where the model has learned to perfectly match the training data but fails to perform well on unseen data.

Strengths

• It provides a simple quantitative measure of the degree to which clusters contain points from only one class.
• Useful for validating clustering solutions where the ground truth — class membership of points — is known.
• It’s agnostic to the absolute labels, and cares only that the points within the same cluster have the same class label.

Limitations

• The Homogeneity Score is not useful for clustering solutions where the ground truth labels are not known.
• It doesn’t work well with differently sized clusters since it gives predominance to larger clusters.
• The score does not address the actual number of clusters formed, or the evenness of cluster sizes. It only checks the homogeneity within the given clusters created by the model.