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sklearn.metrics.davies_bouldin_score(X, labels) [source] ¶. Compute the Davies-Bouldin score. The score is defined as the average similarity measure of each cluster with its most similar cluster, where similarity is the ratio of within-cluster distances to between-cluster distances.
Cada fila corresponde a un único punto de datos. etiquetas en forma de matriz de forma (n_muestras,) Etiquetas previstas para cada muestra. Returns: score: float. La puntuación Davies-Bouldin resultante. References. [1] Davies, David L.; Bouldin, Donald W. (1979). “A Cluster Separation Measure” .
Computes the Davies-Bouldin score. The score is defined as the ratio of within-cluster distances to between-cluster distances. Read more in the User Guide. References. [1] Davies, David L.; Bouldin, Donald W. (1979). “A Cluster Separation Measure”. IEEE Transactions on Pattern Analysis and Machine Intelligence. PAMI-1 (2): 224-227.
The Davies–Bouldin index (DBI), introduced by David L. Davies and Donald W. Bouldin in 1979, is a metric for evaluating clustering algorithms. This is an internal evaluation scheme, where the validation of how well the clustering has been done is made using quantities and features inherent to the dataset.
torchmetrics.functional.clustering. davies_bouldin_score (data, labels) [source] ¶ Compute the Davies bouldin score for clustering algorithms. Parameters: data¶ (Tensor) – float tensor with shape (N,d) with the embedded data. labels¶ (Tensor) – single integer tensor with shape (N,) with cluster labels. Return type: Tensor. Returns:
1 de jun. de 2021 · db_index = davies_bouldin_score(X, labels) print(db_index) You should see the resulting score: 0.7675522686571647 or approximately 0.77 . To put in perspective how the clusters look , let’s visualize them:
31 de ene. de 2021 · sklearn.metrics.calinski_harabasz_score(X, labels) Davies-Bouldin Index. The Davies-Bouldin Index is defined as the average similarity measure of each cluster with its most similar cluster. Similarity is the ratio of within-cluster distances to between-cluster distances.
