sklearn.metrics.calinski_harabaz_score()

sklearn.metrics.calinski_harabaz_score

sklearn.metrics.calinski_harabaz_score(X, labels) [source]

Compute the Calinski and Harabaz score.

The score is defined as ratio between the within-cluster dispersion and the between-cluster dispersion.

Read more in the User Guide.

Parameters:	X : array-like, shape (n_samples, n_features) List of n_features-dimensional data points. Each row corresponds to a single data point. labels : array-like, shape (n_samples,) Predicted labels for each sample.
Returns:	score: float : The resulting Calinski-Harabaz score.

References

[R198]

T. Calinski and J. Harabasz, 1974. “A dendrite method for cluster analysis”. Communications in Statistics