OpenSearch/docs/reference/ml/df-analytics/apis/evaluateresources.asciidoc

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[role="xpack"]
[testenv="platinum"]
[[ml-evaluate-dfanalytics-resources]]
=== {dfanalytics-cap} evaluation resources
Evaluation configuration objects relate to the <<evaluate-dfanalytics>>.
[discrete]
[[ml-evaluate-dfanalytics-properties]]
==== {api-definitions-title}
`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-sc-resources>>.
[[binary-sc-resources]]
==== Binary soft classification configuration objects
Binary soft classification evaluates the results of an analysis which outputs
the probability that each {dataframe} 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.
[discrete]
[[binary-sc-resources-properties]]
===== {api-definitions-title}
`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 {olscore} at where the metric
is calculated.
Default value is {"at": [0.25, 0.50, 0.75]}.
`recall`::
(object) Set the different thresholds of the {olscore} 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 {olscore} 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]}.