Paws::Glue::FindMatchesMetrics
This class represents one of two things:
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 });
Use accessors for each attribute. If Att1 is expected to be an Paws::Glue::FindMatchesMetrics object:
$result = $service_obj->Method(...); $result->Att1->AreaUnderPRCurve
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.
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.
A list of ColumnImportance structures containing column importance metrics, sorted in order of descending importance.
ColumnImportance
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.
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.
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.
This class forms part of Paws, describing an object used in Paws::Glue
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
To install Paws, copy and paste the appropriate command in to your terminal.
cpanm
cpanm Paws
CPAN shell
perl -MCPAN -e shell install Paws
For more information on module installation, please visit the detailed CPAN module installation guide.