AWS SDK for C++
1.8.96
AWS SDK for C++

#include <FindMatchesMetrics.h>
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.
Definition at line 35 of file FindMatchesMetrics.h.
Aws::Glue::Model::FindMatchesMetrics::FindMatchesMetrics  (  ) 
Aws::Glue::Model::FindMatchesMetrics::FindMatchesMetrics  (  Aws::Utils::Json::JsonView  jsonValue  ) 

inline 
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.
Definition at line 62 of file FindMatchesMetrics.h.

inline 
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.
Definition at line 206 of file FindMatchesMetrics.h.

inline 
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.
Definition at line 175 of file FindMatchesMetrics.h.

inline 
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.
Definition at line 52 of file FindMatchesMetrics.h.

inline 
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.
Definition at line 198 of file FindMatchesMetrics.h.

inline 
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.
Definition at line 168 of file FindMatchesMetrics.h.

inline 
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.
Definition at line 92 of file FindMatchesMetrics.h.

inline 
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.
Definition at line 130 of file FindMatchesMetrics.h.
Aws::Utils::Json::JsonValue Aws::Glue::Model::FindMatchesMetrics::Jsonize  (  )  const 
FindMatchesMetrics& Aws::Glue::Model::FindMatchesMetrics::operator=  (  Aws::Utils::Json::JsonView  jsonValue  ) 

inline 
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.
Definition at line 101 of file FindMatchesMetrics.h.

inline 
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.
Definition at line 140 of file FindMatchesMetrics.h.

inline 
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.
Definition at line 72 of file FindMatchesMetrics.h.

inline 
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.
Definition at line 214 of file FindMatchesMetrics.h.

inline 
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.
Definition at line 222 of file FindMatchesMetrics.h.

inline 
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.
Definition at line 182 of file FindMatchesMetrics.h.

inline 
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.
Definition at line 110 of file FindMatchesMetrics.h.

inline 
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.
Definition at line 150 of file FindMatchesMetrics.h.

inline 
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.
Definition at line 82 of file FindMatchesMetrics.h.

inline 
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.
Definition at line 230 of file FindMatchesMetrics.h.

inline 
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.
Definition at line 238 of file FindMatchesMetrics.h.

inline 
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.
Definition at line 189 of file FindMatchesMetrics.h.

inline 
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.
Definition at line 119 of file FindMatchesMetrics.h.

inline 
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.
Definition at line 160 of file FindMatchesMetrics.h.