Builder

The area under the precision/recall curve (AUPRC) is a single number measuring the overall quality of the transform, that is independent of the choice made for precision vs. recall. Higher values indicate that you have a more attractive precision vs. recall tradeoff.

A list of ColumnImportance structures containing column importance metrics, sorted in order of descending importance.

The confusion matrix shows you what your transform is predicting accurately and what types of errors it is making.

The maximum F1 metric indicates the transform's accuracy between 0 and 1, where 1 is the best accuracy.

The precision metric indicates when often your transform is correct when it predicts a match. Specifically, it measures how well the transform finds true positives from the total true positives possible.

The recall metric indicates that for an actual match, how often your transform predicts the match. Specifically, it measures how well the transform finds true positives from the total records in the source data.

construct an aws.sdk.kotlin.services.glue.model.ConfusionMatrix inside the given block