anchor.diff:  [[autoscaling-machine-learning-decider]]
   === Machine learning decider
   
  -The {ml} decider (`ml`) calculates the memory required to run
  -{ml} jobs created by users.
  +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, `xpack.ml.max_lazy_ml_nodes` should be set to the largest
  -number of possible {ml} jobs (see <<advanced-ml-settings>>). In
  -{ess} this is already handled.
  +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 be used to delay a scale-up event. They indicate how many jobs
  -of that type can be unassigned from a node due to the cluster being
  -too small. Both settings are only considered for jobs that could
  -eventually be fully opened given the current scale. If a job is too
  -large for any node size or if a job couldn't ever be assigned without
  -user intervention (for example, a user calling `_stop` against a real-time 
  +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)
  -Number of queued anomaly jobs to allow. Defaults to `0`.
  +Specifies the number of queued {anomaly-jobs} to allow. Defaults to `0`.
   
   `num_analytics_jobs_in_queue`::
   (Optional, integer)
  -Number of queued analytics jobs to allow. Defaults to `0`.
  +Specifies the number of queued {dfanalytics-jobs} to allow. Defaults to `0`.
   
   `down_scale_delay`::
   (Optional, <<time-units,time value>>)
  -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.
  +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 puts an autoscaling policy named `my_autoscaling_policy`,
  -overriding the machine learning decider's configuration.
  +This example creates an autoscaling policy named `my_autoscaling_policy` that 
  +overrides the default configuration of the {ml} decider.
   
   [source,console]
   --------------------------------------------------
  
positive.code:   "acknowledged": true
  }
  --------------------------------------------------
  
  //////////////////////////
  
  
negative.code: --------------------------------------------------
  // TEST
  
  
  The API returns the following result:
  
  [source,console-result]
  