`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

sklearn.metrics.calinski_harabaz_score¶