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