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Data frame analytics evaluation resources

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Data frame analytics evaluation resources

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Evaluation configuration objects relate to the Evaluate data frame analytics.

Properties

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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.

Available evaluation types: * binary_soft_classification * regression

query

(object) A query clause that retrieves a subset of data from the source index. See Query DSL. The evaluation only applies to those documents of the index that match the query.

Binary soft classification configuration objects

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

Properties

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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]}.

Regression evaluation objects

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Regression evaluation evaluates the results of a regression analysis which outputs a prediction of values.

Properties

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actual_field: (string) The field of the index which contains the ground truth. The data type of this field must be numerical.
predicted_field: (string) The field in the index that contains the predicted value, in other words the results of the regression analysis.
metrics: (object) Specifies the metrics that are used for the evaluation. Available metrics are r_squared and mean_squared_error.

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