point-biserial

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Simon-Bertrand / Clusters-Features
The Clusters-Features package allows data science users to compute high-level linear algebra operations on any type of data set. It computes approximatively 40 internal evaluation scores such as Davies-Bouldin Index, C Index, Dunn and its Generalized Indexes and many more ! Other features are also available to evaluate the clustering quality.
clustering indices internal validation ball-hall generalized-dunn-indexes c-index banfeld-raftery davies-bouldin calinski-harabasz ray-turi xie-beni wemmert-gancarski pbm point-biserial unsupervised-learning python clusters evaluation clustering-analysis
Language:Python 29

Simon-Bertrand / Clusters-Features