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Class: Aws::Glue::Types::FindMatchesMetrics
 Inherits:

Struct
 Object
 Struct
 Aws::Glue::Types::FindMatchesMetrics
 Defined in:
 (unknown)
Overview
The evaluation metrics for the find matches algorithm. The quality of your machine learning transform is measured by getting your transform to predict some matches and comparing the results to known matches from the same dataset. The quality metrics are based on a subset of your data, so they are not precise.
Returned by:
Instance Attribute Summary collapse

#area_under_pr_curve ⇒ Float
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.

#confusion_matrix ⇒ Types::ConfusionMatrix
The confusion matrix shows you what your transform is predicting accurately and what types of errors it is making.

#f1 ⇒ Float
The maximum F1 metric indicates the transform\'s accuracy between 0 and 1, where 1 is the best accuracy.

#precision ⇒ Float
The precision metric indicates when often your transform is correct when it predicts a match.

#recall ⇒ Float
The recall metric indicates that for an actual match, how often your transform predicts the match.
Instance Attribute Details
#area_under_pr_curve ⇒ Float
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.
For more information, see Precision and recall in Wikipedia.
#confusion_matrix ⇒ Types::ConfusionMatrix
The confusion matrix shows you what your transform is predicting accurately and what types of errors it is making.
For more information, see Confusion matrix in Wikipedia.
#f1 ⇒ Float
The maximum F1 metric indicates the transform\'s accuracy between 0 and 1, where 1 is the best accuracy.
For more information, see F1 score in Wikipedia.
#precision ⇒ Float
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.
For more information, see Precision and recall in Wikipedia.
#recall ⇒ Float
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.
For more information, see Precision and recall in Wikipedia.