calinski_harabasz_score#
- sklearn.metrics.calinski_harabasz_score(X, labels)[source]#
Compute the Calinski and Harabasz score.
It is also known as the Variance Ratio Criterion.
The score is defined as ratio of the sum of between-cluster dispersion and of within-cluster dispersion.
Read more in the User Guide.
- Parameters:
- Xarray-like of shape (n_samples, n_features)
A list of
n_features-dimensional data points. Each row corresponds to a single data point.- labelsarray-like of shape (n_samples,)
Predicted labels for each sample.
- Returns:
- scorefloat
The resulting Calinski-Harabasz score.
References
Examples
>>> from sklearn.datasets import make_blobs >>> from sklearn.cluster import KMeans >>> from sklearn.metrics import calinski_harabasz_score >>> X, _ = make_blobs(random_state=0) >>> kmeans = KMeans(n_clusters=3, random_state=0,).fit(X) >>> calinski_harabasz_score(X, kmeans.labels_) np.float64(114.8...)