# Daze¶

The `sklearn.metrics`

module allows for the plotting of a confusion matrix from a
classifier (with `sklearn.metrics.plot_confusion_matrix()`

, or directly from a pre-computed confusion matrix (with the internal `sklearn.metrics.ConfusionMatrixDisplay`

class).

A confusion matrix shows the discrepancy between the true labels of a dataset and the labels predicted by a classifier.

While the confusion matrix plots generated by Scikit-Learn are very informative, they omit important evaluation measures that can summarize classification performance. True positives, precision, F1 score and accuracy are example of such measures – all of which can be derived from the confusion matrix. The `sklearn.metrics.classification_report()`

function in the same module provides these measures.

Daze adjusts `plot_confusion_matrix`

to incorporate these evaluation measures directly in the confusion matrix plot, while still maintaining a very similar API
to the original Scikit-Learn function.