Score functions, performance metrics, pairwise metrics and distance computations.
User guide. See the Metrics and scoring: quantifying the quality of predictions and Pairwise metrics, Affinities and Kernels sections for further details.
Model selection interface#User guide. See the The scoring parameter: defining model evaluation rules section for further details.
Classification metrics#User guide. See the Classification metrics section for further details.
Regression metrics#User guide. See the Regression metrics section for further details.
Multilabel ranking metrics#User guide. See the Multilabel ranking metrics section for further details.
Clustering metrics#Evaluation metrics for cluster analysis results.
Supervised evaluation uses a ground truth class values for each sample.
Unsupervised evaluation does not use ground truths and measures the “quality” of the model itself.
User guide. See the Clustering performance evaluation section for further details.
Biclustering metrics#User guide. See the Biclustering evaluation section for further details.
Distance metrics# Pairwise metrics#Metrics for pairwise distances and affinity of sets of samples.
User guide. See the Pairwise metrics, Affinities and Kernels section for further details.
Plotting#User guide. See the Visualizations section for further details.
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