쿠키 기본 설정 선택

당사는 사이트와 서비스를 제공하는 데 필요한 필수 쿠키 및 유사한 도구를 사용합니다. 고객이 사이트를 어떻게 사용하는지 파악하고 개선할 수 있도록 성능 쿠키를 사용해 익명의 통계를 수집합니다. 필수 쿠키는 비활성화할 수 없지만 '사용자 지정' 또는 ‘거부’를 클릭하여 성능 쿠키를 거부할 수 있습니다.

사용자가 동의하는 경우 AWS와 승인된 제3자도 쿠키를 사용하여 유용한 사이트 기능을 제공하고, 사용자의 기본 설정을 기억하고, 관련 광고를 비롯한 관련 콘텐츠를 표시합니다. 필수가 아닌 모든 쿠키를 수락하거나 거부하려면 ‘수락’ 또는 ‘거부’를 클릭하세요. 더 자세한 내용을 선택하려면 ‘사용자 정의’를 클릭하세요.

FindMatchesMetrics - AWS Glue
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FindMatchesMetrics

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.

Contents

AreaUnderPRCurve

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.

Type: Double

Valid Range: Minimum value of 0.0. Maximum value of 1.0.

Required: No

ColumnImportances

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

Type: Array of ColumnImportance objects

Array Members: Minimum number of 0 items. Maximum number of 100 items.

Required: No

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.

Type: ConfusionMatrix object

Required: No

F1

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.

Type: Double

Valid Range: Minimum value of 0.0. Maximum value of 1.0.

Required: No

Precision

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.

Type: Double

Valid Range: Minimum value of 0.0. Maximum value of 1.0.

Required: No

Recall

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.

Type: Double

Valid Range: Minimum value of 0.0. Maximum value of 1.0.

Required: No

See Also

For more information about using this API in one of the language-specific AWS SDKs, see the following:

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