CompletenessScore

Evaluates a clustering model’s capacity to categorize instances from a single class into the same cluster.

Purpose

The Completeness Score metric is used to assess the performance of clustering models. It measures the extent to which all the data points that are members of a given class are elements of the same cluster. The aim is to determine the capability of the model to categorize all instances from a single class into the same cluster.

Test Mechanism

This test takes three inputs, a model and its associated training and testing datasets. It invokes the completeness_score function from the sklearn library on the labels predicted by the model. High scores indicate that data points from the same class generally appear in the same cluster, while low scores suggest the opposite.

Signs of High Risk

• Low completeness score: This suggests that the model struggles to group instances from the same class into one cluster, indicating poor clustering performance.

Strengths

• The Completeness Score provides an effective method for assessing the performance of a clustering model, specifically its ability to group class instances together.
• This test metric conveniently relies on the capabilities provided by the sklearn library, ensuring consistent and reliable test results.

Limitations

• This metric only evaluates a specific aspect of clustering, meaning it may not provide a holistic or complete view of the model’s performance.
• It cannot assess the effectiveness of the model in differentiating between separate classes, as it is solely focused on how well data points from the same class are grouped.
• The Completeness Score only applies to clustering models; it cannot be used for other types of machine learning models.