sklearn.metrics (original) (raw)

• API Reference
• sklearn.metrics

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 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.