49 lines
1.7 KiB
Python
49 lines
1.7 KiB
Python
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"""
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The :mod:`sklearn.metrics.cluster` submodule contains evaluation metrics for
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cluster analysis results. There are two forms of evaluation:
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- supervised, which uses a ground truth class values for each sample.
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- unsupervised, which does not and measures the 'quality' of the model itself.
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"""
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from ._supervised import adjusted_mutual_info_score
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from ._supervised import normalized_mutual_info_score
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from ._supervised import adjusted_rand_score
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from ._supervised import rand_score
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from ._supervised import completeness_score
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from ._supervised import contingency_matrix
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from ._supervised import pair_confusion_matrix
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from ._supervised import expected_mutual_information
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from ._supervised import homogeneity_completeness_v_measure
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from ._supervised import homogeneity_score
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from ._supervised import mutual_info_score
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from ._supervised import v_measure_score
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from ._supervised import fowlkes_mallows_score
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from ._supervised import entropy
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from ._unsupervised import silhouette_samples
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from ._unsupervised import silhouette_score
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from ._unsupervised import calinski_harabasz_score
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from ._unsupervised import davies_bouldin_score
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from ._bicluster import consensus_score
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__all__ = [
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"adjusted_mutual_info_score",
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"normalized_mutual_info_score",
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"adjusted_rand_score",
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"rand_score",
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"completeness_score",
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"pair_confusion_matrix",
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"contingency_matrix",
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"expected_mutual_information",
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"homogeneity_completeness_v_measure",
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"homogeneity_score",
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"mutual_info_score",
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"v_measure_score",
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"fowlkes_mallows_score",
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"entropy",
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"silhouette_samples",
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"silhouette_score",
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"calinski_harabasz_score",
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"davies_bouldin_score",
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"consensus_score",
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]
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