scikitplot.metrics

This package/module is designed to be compatible with both Python 2 and Python 3. The imports below ensure consistent behavior across different Python versions by enforcing Python 3-like behavior in Python 2.

The metrics module includes plots for machine learning evaluation metrics e.g. confusion matrix, silhouette scores, etc.

User guide. See the Metrics section for further details.

Model selection interface

User guide. See the Metrics section for further details.

plot_calibration_curve

Plot calibration curves for a set of classifier probability estimates.

Classification metrics

User guide. See the Metrics section for further details.

plot_classifier_eval	Generates various evaluation plots for a classifier, including confusion matrix, precision-recall curve, and ROC curve.
plot_confusion_matrix	Generates a confusion matrix plot from predictions and true labels.
plot_roc	Generates the ROC AUC curves from labels and predicted scores/probabilities.
plot_precision_recall	Generates the Precision-Recall AUC Curves from labels and predicted scores/probabilities.

Clustering metrics

User guide. See the Metrics section for further details.

plot_silhouette

Plots silhouette analysis of clusters provided.