63 lines
2.2 KiB
Plaintext
63 lines
2.2 KiB
Plaintext
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[role="xpack"]
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[testenv="platinum"]
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[[ml-evaluate-dfanalytics-resources]]
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=== {dfanalytics-cap} evaluation resources
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Evaluation configuration objects relate to the <<evaluate-dfanalytics>>.
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[discrete]
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[[ml-evaluate-dfanalytics-properties]]
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==== {api-definitions-title}
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`evaluation`::
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(object) Defines the type of evaluation you want to perform. The value of this
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object can be different depending on the type of evaluation you want to
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perform. For example, it can contain <<binary-sc-resources>>.
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[[binary-sc-resources]]
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==== Binary soft classification configuration objects
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Binary soft classification evaluates the results of an analysis which outputs
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the probability that each {dataframe} row belongs to a certain class. For
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example, in the context of outlier detection, the analysis outputs the
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probability whether each row is an outlier.
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[discrete]
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[[binary-sc-resources-properties]]
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===== {api-definitions-title}
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`actual_field`::
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(string) The field of the `index` which contains the `ground
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truth`. The data type of this field can be boolean or integer. If the data
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type is integer, the value has to be either `0` (false) or `1` (true).
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`predicted_probability_field`::
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(string) The field of the `index` that defines the probability of whether the
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item belongs to the class in question or not. It's the field that contains the
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results of the analysis.
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`metrics`::
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(object) Specifies the metrics that are used for the evaluation. Available
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metrics:
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`auc_roc`::
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(object) The AUC ROC (area under the curve of the receiver operating
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characteristic) score and optionally the curve.
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Default value is {"includes_curve": false}.
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`precision`::
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(object) Set the different thresholds of the {olscore} at where the metric
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is calculated.
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Default value is {"at": [0.25, 0.50, 0.75]}.
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`recall`::
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(object) Set the different thresholds of the {olscore} at where the metric
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is calculated.
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Default value is {"at": [0.25, 0.50, 0.75]}.
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`confusion_matrix`::
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(object) Set the different thresholds of the {olscore} at where the metrics
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(`tp` - true positive, `fp` - false positive, `tn` - true negative, `fn` -
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false negative) are calculated.
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Default value is {"at": [0.25, 0.50, 0.75]}.
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