Calculating the completeness score using sklearn in Python
An entirely complete clustering is one where each cluster has information that directs a place toward a similar class cluster. Completeness portrays the closeness of the clustering algorithm to this (completeness_score) perfection.
This metric is autonomous of the outright values of the labels. A permutation of the cluster label values won’t change the score value in any way.
Syntax: sklearn.metrics.completeness_score(labels_true, labels_pred)
- labels_true:<int array, shape = [n_samples]>: It accepts the ground truth class labels to be used as a reference.
- labels_pred: <array-like of shape (n_samples,)>: It accepts the cluster labels to evaluate.
Returns: completeness score between 0.0 and 1.0. 1.0 stands for perfectly completeness labeling.
Switching label_true with label_pred will return the homogeneity_score.
Example 2: Perfectly completeness:
Example 3: Non-perfect labeling that further split classes into more clusters can be perfectly completeness:
Example 4: Include samples from different classes don’t make for completeness labeling: