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:

• EvaluationMetrics#find_matches_metrics

See Also:

• AWS API Documentation

Instance Attribute Summary

• #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.

Returns:

• (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.

For more information, see Confusion matrix in Wikipedia.

Returns:

• (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.

For more information, see F1 score in Wikipedia.

Returns:

• (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. 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.

Returns:

• (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. 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.

Returns:

• (Float) —

  The recall metric indicates that for an actual match, how often your transform predicts the match.