The Silhouette Coefficient is used when the ground-truth about the dataset is unknown and computes the density of clusters computed by the model. The score is computed by averaging the silhouette coefficient for each sample, computed as the difference between the average intra-cluster distance and the mean nearest-cluster distance for each sample, normalized by the maximum value. This produces a score between 1 and -1, where 1 is highly dense clusters and -1 is completely incorrect clustering.
The Silhouette Visualizer displays the silhouette coefficient for each sample on a per-cluster basis, visualizing which clusters are dense and which are not. This is particularly useful for determining cluster imbalance, or for selecting a value for \(K\) by comparing multiple visualizers.
from sklearn.cluster import KMeans from yellowbrick.cluster import SilhouetteVisualizer from yellowbrick.datasets import load_nfl # Load a clustering dataset X, y = load_nfl() # Specify the features to use for clustering features = ['Rec', 'Yds', 'TD', 'Fmb', 'Ctch_Rate'] X = X.query('Tgt >= 20')[features] # Instantiate the clustering model and visualizer model = KMeans(5, random_state=42) visualizer = SilhouetteVisualizer(model, colors='yellowbrick') visualizer.fit(X) # Fit the data to the visualizer visualizer.show() # Finalize and render the figure
Implements visualizers that use the silhouette metric for cluster evaluation.
SilhouetteVisualizer(model, ax=None, colors=None, is_fitted='auto', **kwargs)¶
The Silhouette Visualizer displays the silhouette coefficient for each sample on a per-cluster basis, visually evaluating the density and separation between clusters. The score is calculated by averaging the silhouette coefficient for each sample, computed as the difference between the average intra-cluster distance and the mean nearest-cluster distance for each sample, normalized by the maximum value. This produces a score between -1 and +1, where scores near +1 indicate high separation and scores near -1 indicate that the samples may have been assigned to the wrong cluster.
In SilhouetteVisualizer plots, clusters with higher scores have wider silhouettes, but clusters that are less cohesive will fall short of the average score across all clusters, which is plotted as a vertical dotted red line.
This is particularly useful for determining cluster imbalance, or for selecting a value for K by comparing multiple visualizers.
- modela Scikit-Learn clusterer
Should be an instance of a centroidal clustering algorithm (
MiniBatchKMeans). If the estimator is not fitted, it is fit when the visualizer is fitted, unless otherwise specified by
- axmatplotlib Axes, default: None
The axes to plot the figure on. If None is passed in the current axes will be used (or generated if required).
- colorsiterable or string, default: None
A collection of colors to use for each cluster group. If there are fewer colors than cluster groups, colors will repeat. May also be a Yellowbrick or matplotlib colormap string.
- is_fittedbool or str, default=’auto’
Specify if the wrapped estimator is already fitted. If False, the estimator will be fit when the visualizer is fit, otherwise, the estimator will not be modified. If ‘auto’ (default), a helper method will check if the estimator is fitted before fitting it again.
Keyword arguments that are passed to the base class and may influence the visualization as defined in other Visualizers.
>>> from yellowbrick.cluster import SilhouetteVisualizer >>> from sklearn.cluster import KMeans >>> model = SilhouetteVisualizer(KMeans(10)) >>> model.fit(X) >>> model.show()
Mean Silhouette Coefficient for all samples. Computed via scikit-learn sklearn.metrics.silhouette_score.
- silhouette_samples_array, shape = [n_samples]
Silhouette Coefficient for each samples. Computed via scikit-learn sklearn.metrics.silhouette_samples.
Number of total samples in the dataset (X.shape)
Number of clusters (e.g. n_clusters or k value) passed to internal scikit-learn model.
- y_tick_pos_array of shape (n_clusters,)
The computed center positions of each cluster on the y-axis
Draw the silhouettes for each sample and the average score.
An array with the cluster label for each silhouette sample, usually computed with
predict(). Labels are not stored on the visualizer so that the figure can be redrawn with new data.
Prepare the figure for rendering by setting the title and adjusting the limits on the axes, adding labels and a legend.
fit(self, X, y=None, **kwargs)¶
Fits the model and generates the silhouette visualization.