53 lines
1.4 KiB
Python
53 lines
1.4 KiB
Python
"""
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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 ._bicluster import consensus_score
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from ._supervised import (
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adjusted_mutual_info_score,
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adjusted_rand_score,
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completeness_score,
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contingency_matrix,
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entropy,
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expected_mutual_information,
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fowlkes_mallows_score,
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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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normalized_mutual_info_score,
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pair_confusion_matrix,
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rand_score,
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v_measure_score,
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)
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from ._unsupervised import (
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calinski_harabasz_score,
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davies_bouldin_score,
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silhouette_samples,
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silhouette_score,
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)
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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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