/ 
Paws-0.46
River stage two • 9 direct dependents • 11 total dependents
/ Paws::Glue::FindMatchesMetrics

NAME

Paws::Glue::FindMatchesMetrics

USAGE

This class represents one of two things:

Arguments in a call to a service

Use the attributes of this class as arguments to methods. You shouldn't make instances of this class. Each attribute should be used as a named argument in the calls that expect this type of object.

As an example, if Att1 is expected to be a Paws::Glue::FindMatchesMetrics object:

$service_obj->Method(Att1 => { AreaUnderPRCurve => $value, ..., Recall => $value  });

Results returned from an API call

Use accessors for each attribute. If Att1 is expected to be an Paws::Glue::FindMatchesMetrics object:

$result = $service_obj->Method(...);
$result->Att1->AreaUnderPRCurve

DESCRIPTION

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.

ATTRIBUTES

AreaUnderPRCurve => Num

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 (https://en.wikipedia.org/wiki/Precision_and_recall) in Wikipedia.

ColumnImportances => ArrayRef[Paws::Glue::ColumnImportance]

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

ConfusionMatrix => Paws::Glue::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 (https://en.wikipedia.org/wiki/Confusion_matrix) in Wikipedia.

F1 => Num

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 (https://en.wikipedia.org/wiki/F1_score) in Wikipedia.

Precision => Num

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 (https://en.wikipedia.org/wiki/Precision_and_recall) in Wikipedia.

Recall => Num

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 (https://en.wikipedia.org/wiki/Precision_and_recall) in Wikipedia.

SEE ALSO

This class forms part of Paws, describing an object used in Paws::Glue

BUGS and CONTRIBUTIONS

The source code is located here: https://github.com/pplu/aws-sdk-perl

Please report bugs to: https://github.com/pplu/aws-sdk-perl/issues