32 lines
1.5 KiB
Python
32 lines
1.5 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 ._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 completeness_score
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from ._supervised import contingency_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__ = ["adjusted_mutual_info_score", "normalized_mutual_info_score",
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"adjusted_rand_score", "completeness_score", "contingency_matrix",
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"expected_mutual_information", "homogeneity_completeness_v_measure",
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"homogeneity_score", "mutual_info_score", "v_measure_score",
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"fowlkes_mallows_score", "entropy", "silhouette_samples",
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"silhouette_score", "calinski_harabasz_score",
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"davies_bouldin_score", "consensus_score"]
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