/AWS1/CL_FDTTRAININGMETRICS¶
The training metric details.
CONSTRUCTOR¶
IMPORTING¶
Optional arguments:¶
iv_auc TYPE /AWS1/RT_FLOAT_AS_STRING /AWS1/RT_FLOAT_AS_STRING¶
The area under the curve. This summarizes true positive rate (TPR) and false positive rate (FPR) across all possible model score thresholds. A model with no predictive power has an AUC of 0.5, whereas a perfect model has a score of 1.0.
it_metricdatapoints TYPE /AWS1/CL_FDTMETRICDATAPOINT=>TT_METRICDATAPOINTSLIST TT_METRICDATAPOINTSLIST¶
The data points details.
Queryable Attributes¶
auc¶
The area under the curve. This summarizes true positive rate (TPR) and false positive rate (FPR) across all possible model score thresholds. A model with no predictive power has an AUC of 0.5, whereas a perfect model has a score of 1.0.
Accessible with the following methods¶
| Method | Description |
|---|---|
GET_AUC() |
Getter for AUC, with configurable default |
ASK_AUC() |
Getter for AUC w/ exceptions if field has no value |
STR_AUC() |
String format for AUC, with configurable default |
HAS_AUC() |
Determine if AUC has a value |
metricDataPoints¶
The data points details.
Accessible with the following methods¶
| Method | Description |
|---|---|
GET_METRICDATAPOINTS() |
Getter for METRICDATAPOINTS, with configurable default |
ASK_METRICDATAPOINTS() |
Getter for METRICDATAPOINTS w/ exceptions if field has no va |
HAS_METRICDATAPOINTS() |
Determine if METRICDATAPOINTS has a value |