Data frame analytics evaluation resourcesedit

Evaluation configuration objects relate to the Evaluate data frame analytics.

Propertiesedit

evaluation
(object) Defines the type of evaluation you want to perform. The value of this object can be different depending on the type of evaluation you want to perform. For example, it can contain Binary soft classification configuration objects.

Binary soft classification configuration objectsedit

Binary soft classification evaluates the results of an analysis which outputs the probability that each data frame row belongs to a certain class. For example, in the context of outlier detection, the analysis outputs the probability whether each row is an outlier.

Propertiesedit
actual_field
(string) The field of the index which contains the ground truth. The data type of this field can be boolean or integer. If the data type is integer, the value has to be either 0 (false) or 1 (true).
predicted_probability_field
(string) The field of the index that defines the probability of whether the item belongs to the class in question or not. It’s the field that contains the results of the analysis.
metrics
(object) Specifies the metrics that are used for the evaluation. Available metrics:
auc_roc
(object) The AUC ROC (area under the curve of the receiver operating characteristic) score and optionally the curve. Default value is {"includes_curve": false}.
precision
(object) Set the different thresholds of the outlier score at where the metric is calculated. Default value is {"at": [0.25, 0.50, 0.75]}.
recall
(object) Set the different thresholds of the outlier score at where the metric is calculated. Default value is {"at": [0.25, 0.50, 0.75]}.
confusion_matrix
(object) Set the different thresholds of the outlier score at where the metrics (tp - true positive, fp - false positive, tn - true negative, fn - false negative) are calculated. Default value is {"at": [0.25, 0.50, 0.75]}.