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FindMatchesMetrics

Source code in docs_src/capo_glue/types/find_matches_metrics.py 
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class FindMatchesMetrics(TypedDict, closed=True):
    area_under_pr_curve: NotRequired[
        "capo_glue.types.generic_bounded_double.GenericBoundedDouble"
    ]
    """<p>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.</p> <p>For more information, see <a href="https://en.wikipedia.org/wiki/Precision_and_recall">Precision and recall</a> in Wikipedia.</p>"""
    precision: NotRequired[
        "capo_glue.types.generic_bounded_double.GenericBoundedDouble"
    ]
    """<p>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.</p> <p>For more information, see <a href="https://en.wikipedia.org/wiki/Precision_and_recall">Precision and recall</a> in Wikipedia.</p>"""
    recall: NotRequired["capo_glue.types.generic_bounded_double.GenericBoundedDouble"]
    """<p>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.</p> <p>For more information, see <a href="https://en.wikipedia.org/wiki/Precision_and_recall">Precision and recall</a> in Wikipedia.</p>"""
    f1: NotRequired["capo_glue.types.generic_bounded_double.GenericBoundedDouble"]
    """<p>The maximum F1 metric indicates the transform's accuracy between 0 and 1, where 1 is the best accuracy.</p> <p>For more information, see <a href="https://en.wikipedia.org/wiki/F1_score">F1 score</a> in Wikipedia.</p>"""
    confusion_matrix: NotRequired["capo_glue.types.confusion_matrix.ConfusionMatrix"]
    """<p>The confusion matrix shows you what your transform is predicting accurately and what types of errors it is making.</p> <p>For more information, see <a href="https://en.wikipedia.org/wiki/Confusion_matrix">Confusion matrix</a> in Wikipedia.</p>"""
    column_importances: NotRequired[
        "capo_glue.types.column_importance_list.ColumnImportanceList"
    ]
    """<p>A list of <code>ColumnImportance</code> structures containing column importance metrics, sorted in order of descending importance.</p>"""

area_under_pr_curve instance-attribute 

area_under_pr_curve: NotRequired[GenericBoundedDouble]

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.

column_importances instance-attribute 

column_importances: NotRequired[ColumnImportanceList]

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

confusion_matrix instance-attribute 

confusion_matrix: NotRequired[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.

f1 instance-attribute 

f1: NotRequired[GenericBoundedDouble]

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.

precision instance-attribute 

precision: NotRequired[GenericBoundedDouble]

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.

recall instance-attribute 

recall: NotRequired[GenericBoundedDouble]

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.