anchor.diff:  (`num_top_classes`). The result is written to the `prediction` field and the top 
   classes to the `probabilities` field. Both fields are contained in the 
   `target_field` results object.
  +
  +
  +[discrete]
  +[[inference-processor-feature-importance]]
  +==== {feat-imp-cap} object mapping
  +
  +Update your index mapping of the {feat-imp} result field as you can see below to 
  +get the full benefit of aggregating and searching for 
  +{ml-docs}/dfa-classification.html#dfa-classification-feature-importance[{feat-imp}].
  +
  +[source,js]
  +--------------------------------------------------
  +"ml.inference.feature_importance": {
  +  "type": "nested",
  +  "dynamic": true,
  +  "properties": {
  +    "feature_name": {
  +      "type": "keyword"
  +    },
  +    "importance": {
  +      "type": "double"
  +    }
  +  }
  +}
  +--------------------------------------------------
  +// NOTCONSOLE
  +
  +The mapping field name for {feat-imp} is compounded as follows:
  +
  +`<ml.inference.target_field>`.`<inference.tag>`.`feature_importance`
  +
  +If `inference.tag` is not provided in the processor definition, it is not part 
  +of the field path. The `<ml.inference.target_field>` defaults to `ml.inference`.
  +
  +For example, you provide a tag `foo` in the definition as you can see below:
  +
  +[source,js]
  +--------------------------------------------------
  +{
  +  "tag": "foo",
  +  ...
  +}
  +--------------------------------------------------
  +// NOTCONSOLE
  +
  +
  +The `{feat-imp}` value is written to the `ml.inference.foo.feature_importance` 
  +field.
  +
  +You can also specify a target field as follows:
  +
  +[source,js]
  +--------------------------------------------------
  +{
  +  "tag": "foo",
  +  "target_field": "my_field"
  +}
  +--------------------------------------------------
  +// NOTCONSOLE
  +
  +In this case, `{feat-imp}` is exposed in the 
  +`my_field.foo.feature_importance` field.
positive.code: {ml-docs}/dfa-regression.html#dfa-regression-feature-importance[feature
  importance] values per document. By default, it is zero and no feature importance
  calculation occurs.
  
  [discrete]
  [[inference-processor-classification-opt]]
  
negative.code: // NOTCONSOLE
  
  
  [discrete]
  [[inference-processor-regression-opt]]
  ==== {regression-cap} configuration options
  