182 lines
5.6 KiB
Python
182 lines
5.6 KiB
Python
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"""
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The :mod:`sklearn.metrics` module includes score functions, performance metrics
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and pairwise metrics and distance computations.
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"""
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from ._ranking import auc
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from ._ranking import average_precision_score
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from ._ranking import coverage_error
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from ._ranking import det_curve
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from ._ranking import dcg_score
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from ._ranking import label_ranking_average_precision_score
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from ._ranking import label_ranking_loss
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from ._ranking import ndcg_score
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from ._ranking import precision_recall_curve
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from ._ranking import roc_auc_score
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from ._ranking import roc_curve
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from ._ranking import top_k_accuracy_score
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from ._classification import accuracy_score
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from ._classification import balanced_accuracy_score
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from ._classification import class_likelihood_ratios
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from ._classification import classification_report
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from ._classification import cohen_kappa_score
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from ._classification import confusion_matrix
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from ._classification import f1_score
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from ._classification import fbeta_score
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from ._classification import hamming_loss
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from ._classification import hinge_loss
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from ._classification import jaccard_score
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from ._classification import log_loss
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from ._classification import matthews_corrcoef
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from ._classification import precision_recall_fscore_support
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from ._classification import precision_score
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from ._classification import recall_score
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from ._classification import zero_one_loss
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from ._classification import brier_score_loss
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from ._classification import multilabel_confusion_matrix
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from ._dist_metrics import DistanceMetric
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from . import cluster
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from .cluster import adjusted_mutual_info_score
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from .cluster import adjusted_rand_score
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from .cluster import rand_score
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from .cluster import pair_confusion_matrix
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from .cluster import completeness_score
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from .cluster import consensus_score
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from .cluster import homogeneity_completeness_v_measure
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from .cluster import homogeneity_score
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from .cluster import mutual_info_score
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from .cluster import normalized_mutual_info_score
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from .cluster import fowlkes_mallows_score
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from .cluster import silhouette_samples
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from .cluster import silhouette_score
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from .cluster import calinski_harabasz_score
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from .cluster import v_measure_score
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from .cluster import davies_bouldin_score
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from .pairwise import euclidean_distances
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from .pairwise import nan_euclidean_distances
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from .pairwise import pairwise_distances
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from .pairwise import pairwise_distances_argmin
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from .pairwise import pairwise_distances_argmin_min
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from .pairwise import pairwise_kernels
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from .pairwise import pairwise_distances_chunked
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from ._regression import explained_variance_score
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from ._regression import max_error
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from ._regression import mean_absolute_error
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from ._regression import mean_squared_error
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from ._regression import mean_squared_log_error
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from ._regression import median_absolute_error
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from ._regression import mean_absolute_percentage_error
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from ._regression import mean_pinball_loss
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from ._regression import r2_score
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from ._regression import mean_tweedie_deviance
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from ._regression import mean_poisson_deviance
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from ._regression import mean_gamma_deviance
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from ._regression import d2_tweedie_score
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from ._regression import d2_pinball_score
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from ._regression import d2_absolute_error_score
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from ._scorer import check_scoring
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from ._scorer import make_scorer
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from ._scorer import SCORERS
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from ._scorer import get_scorer
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from ._scorer import get_scorer_names
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from ._plot.det_curve import DetCurveDisplay
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from ._plot.roc_curve import RocCurveDisplay
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from ._plot.precision_recall_curve import PrecisionRecallDisplay
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from ._plot.confusion_matrix import ConfusionMatrixDisplay
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from ._plot.regression import PredictionErrorDisplay
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__all__ = [
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"accuracy_score",
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"adjusted_mutual_info_score",
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"adjusted_rand_score",
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"auc",
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"average_precision_score",
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"balanced_accuracy_score",
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"calinski_harabasz_score",
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"check_scoring",
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"class_likelihood_ratios",
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"classification_report",
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"cluster",
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"cohen_kappa_score",
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"completeness_score",
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"ConfusionMatrixDisplay",
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"confusion_matrix",
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"consensus_score",
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"coverage_error",
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"d2_tweedie_score",
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"d2_absolute_error_score",
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"d2_pinball_score",
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"dcg_score",
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"davies_bouldin_score",
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"DetCurveDisplay",
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"det_curve",
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"DistanceMetric",
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"euclidean_distances",
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"explained_variance_score",
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"f1_score",
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"fbeta_score",
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"fowlkes_mallows_score",
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"get_scorer",
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"hamming_loss",
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"hinge_loss",
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"homogeneity_completeness_v_measure",
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"homogeneity_score",
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"jaccard_score",
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"label_ranking_average_precision_score",
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"label_ranking_loss",
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"log_loss",
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"make_scorer",
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"nan_euclidean_distances",
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"matthews_corrcoef",
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"max_error",
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"mean_absolute_error",
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"mean_squared_error",
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"mean_squared_log_error",
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"mean_pinball_loss",
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"mean_poisson_deviance",
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"mean_gamma_deviance",
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"mean_tweedie_deviance",
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"median_absolute_error",
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"mean_absolute_percentage_error",
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"multilabel_confusion_matrix",
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"mutual_info_score",
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"ndcg_score",
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"normalized_mutual_info_score",
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"pair_confusion_matrix",
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"pairwise_distances",
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"pairwise_distances_argmin",
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"pairwise_distances_argmin_min",
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"pairwise_distances_chunked",
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"pairwise_kernels",
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"PrecisionRecallDisplay",
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"precision_recall_curve",
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"precision_recall_fscore_support",
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"precision_score",
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"PredictionErrorDisplay",
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"r2_score",
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"rand_score",
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"recall_score",
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"RocCurveDisplay",
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"roc_auc_score",
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"roc_curve",
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"SCORERS",
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"get_scorer_names",
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"silhouette_samples",
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"silhouette_score",
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"top_k_accuracy_score",
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"v_measure_score",
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"zero_one_loss",
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"brier_score_loss",
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]
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