Learning Data Mining with Python by 2015

Author:2015
Language: eng
Format: mobi
Publisher: Packt Publishing

However, the values are based on our weights, which are a similarity and not a distance. For a distance, higher values indicate more difference. We can convert from similarity to distance by subtracting the value from the maximum possible value, which for our weights was 1:

X = 1 - X

Now we have our distance matrix and labels, so we have all the information we need to compute the Silhouette Coefficient. We pass the metric as precomputed; otherwise, the matrix X will be considered a feature matrix, not a distance matrix (feature matrices are used by default nearly everywhere in scikit-learn). The code is as follows:

return silhouette_score(X, labels, metric='precomputed')

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