init(classificationError:confusion:precisionRecall:)

Declaration

init(classificationError: Double, confusion: MLDataTable, precisionRecall: MLDataTable)

Parameters

• classificationError:

  The fraction of incorrectly labeled examples.

• confusion:

  A confusion matrix describing the classifications for each category.

• precisionRecall:

  A two-dimensional table describing the precision and recall for each category.

Discussion

You typically don’t initialize metrics directly. Instead you get metrics about your model after training. For example, when you train an MLClassifier, you can look at its trainingMetrics and validationMetrics properties. Additionally, you can check the performance on a test set with the evaluation(on:) method.