anchor.diff:    "acknowledged": true
   }
   --------------------------------------------------
  +
   
   //////////////////////////
   
  
positive.code: --------------------------------------------------
  // TEST
  
  The API returns the following result:
  
  [source,console-result]
  
negative.code: [[autoscaling-machine-learning-decider]]
  === Machine learning decider
  
  The {ml} decider (`ml`) calculates the memory required to run {ml} jobs.
  
  The {ml} decider is enabled for policies governing `ml` nodes.
  
  NOTE: For {ml} jobs to open when the cluster is not appropriately
  scaled, set `xpack.ml.max_lazy_ml_nodes` to the largest number of possible {ml} 
  jobs (refer to <<advanced-ml-settings>> for more information). In {ess}, this is 
  automatically set.
  
  [[autoscaling-machine-learning-decider-settings]]
  ==== Configuration settings
  
  Both `num_anomaly_jobs_in_queue` and `num_analytics_jobs_in_queue` are designed 
  to delay a scale-up event. If the cluster is too small, these settings indicate how many jobs of each type can be 
  unassigned from a node. Both settings are 
  only considered for jobs that can be opened given the current scale. If a job is 
  too large for any node size or if a job can't be assigned without user 
  intervention (for example, a user calling `_stop` against a real-time 
  {anomaly-job}), the numbers are ignored for that particular job.
  
  `num_anomaly_jobs_in_queue`::
  (Optional, integer)
  Specifies the number of queued {anomaly-jobs} to allow. Defaults to `0`.
  
  `num_analytics_jobs_in_queue`::
  (Optional, integer)
  Specifies the number of queued {dfanalytics-jobs} to allow. Defaults to `0`.
  
  `down_scale_delay`::
  (Optional, <<time-units,time value>>)
  Specifies the time to delay before scaling down. Defaults to 1 hour. If a scale 
  down is possible for the entire time window, then a scale down is requested. If 
  the cluster requires a scale up during the window, the window is reset.
  
  
  [[autoscaling-machine-learning-decider-examples]]
  ==== {api-examples-title}
  
  This example creates an autoscaling policy named `my_autoscaling_policy` that 
  overrides the default configuration of the {ml} decider.
  
  [source,console]
  --------------------------------------------------
  