[role="xpack"]
[testenv="basic"]
[[inference-processor]]
=== {infer-cap} Processor

Uses a pre-trained {dfanalytics} model to infer against the data that is being 
ingested in the pipeline.


[[inference-options]]
.{infer-cap} Options
[options="header"]
|======
| Name               | Required  | Default                        | Description
| `model_id`         | yes       | -                              | (String) The ID of the model to load and infer against.
| `target_field`     | no        | `ml.inference.<processor_tag>` | (String) Field added to incoming documents to contain results objects.
| `field_mappings`   | yes       | -                              | (Object) Maps the document field names to the known field names of the model.
| `inference_config` | yes       | -                              | (Object) Contains the inference type and its options. There are two types: <<inference-processor-regression-opt,`regression`>> and <<inference-processor-classification-opt,`classification`>>.
include::common-options.asciidoc[]
|======


[source,js]
--------------------------------------------------
{
  "inference": {
    "model_id": "flight_delay_regression-1571767128603",
    "target_field": "FlightDelayMin_prediction_infer",
    "field_mappings": {},
    "inference_config": { "regression": {} }
  }
}
--------------------------------------------------
// NOTCONSOLE



[discrete]
[[inference-processor-regression-opt]]
==== {regression-cap} configuration options

`results_field`::
(Optional, string)
Specifies the field to which the inference prediction is written. Defaults to 
`predicted_value`.

`num_top_feature_importance_values`::::
(Optional, integer)
Specifies the maximum number of
{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]]
==== {classification-cap} configuration options

`results_field`::
(Optional, string)
The field that is added to incoming documents to contain the inference prediction. Defaults to 
`predicted_value`.

`num_top_classes`::
(Optional, integer) 
Specifies the number of top class predictions to return. Defaults to 0.

`top_classes_results_field`::
(Optional, string) 
Specifies the field to which the top classes are written. Defaults to 
`top_classes`.

`num_top_feature_importance_values`::::
(Optional, integer)
Specifies the maximum number of
{ml-docs}/dfa-classification.html#dfa-classification-feature-importance[feature
importance] values per document. By default, it is zero and no feature importance
calculation occurs.

[discrete]
[[inference-processor-config-example]]
==== `inference_config` examples

[source,js]
--------------------------------------------------
{
  "inference_config": {
    "regression": {
      "results_field": "my_regression"
    }
  }
}
--------------------------------------------------
// NOTCONSOLE

This configuration specifies a `regression` inference and the results are 
written to the `my_regression` field contained in the `target_field` results 
object.


[source,js]
--------------------------------------------------
{
  "inference_config": {
    "classification": {
      "num_top_classes": 2,
      "results_field": "prediction",
      "top_classes_results_field": "probabilities"
    }
  }
}
--------------------------------------------------
// NOTCONSOLE

This configuration specifies a `classification` inference. The number of 
categories for which the predicted probabilities are reported is 2 
(`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.