missing documentation for exclude_finished_job

Author: amibp

articles/machine-learning/how-to-tune-hyperparameters.md

@@ -190,7 +190,6 @@ You can configure the following parameters that control when a policy is applied
 
 * `evaluation_interval`: the frequency of applying the policy. Each time the training script logs the primary metric counts as one interval. An `evaluation_interval` of 1 will apply the policy every time the training script reports the primary metric. An `evaluation_interval` of 2 will apply the policy every other time. If not specified, `evaluation_interval` is set to 1 by default.
 * `delay_evaluation`: delays the first policy evaluation for a specified number of intervals. This is an optional parameter that avoids premature termination of training runs by allowing all configurations to run for a minimum number of intervals. If specified, the policy applies every multiple of evaluation_interval that is greater than or equal to delay_evaluation.
-* `exclude_finished_jobs`: specifies whether to exclude finished jobs as part of the early termination policy. This is an optional parameter that ______. If specified, the policy _____.
 
 Azure Machine Learning supports the following early termination policies:
 * [Bandit policy](#bandit-policy)
@@ -213,7 +212,6 @@ Specify the following configuration parameters:
     For example,  consider a Bandit policy applied at interval 10. Assume that the best performing run at interval 10 reported a primary metric is 0.8 with a goal to maximize the primary metric. If the policy specifies a `slack_factor` of 0.2, any training runs whose best metric at interval 10 is less than 0.66 (0.8/(1+`slack_factor`)) will be terminated.
 * `evaluation_interval`: (optional) the frequency for applying the policy
 * `delay_evaluation`: (optional) delays the first policy evaluation for a specified number of intervals
-* `exclude_finished_jobs`: (optional) specifies whether to exclude finished jobs as part of the early termination policy
 
 
 
@@ -231,7 +229,6 @@ In this example, the early termination policy is applied at every interval when
 This policy takes the following configuration parameters:
 * `evaluation_interval`: the frequency for applying the policy (optional parameter).
 * `delay_evaluation`: delays the first policy evaluation for a specified number of intervals (optional parameter).
-* `exclude_finished_jobs`: (optional) specifies whether to exclude finished jobs as part of the early termination policy
 
 
 ```Python
@@ -250,15 +247,15 @@ This policy takes the following configuration parameters:
 * `truncation_percentage`: the percentage of lowest performing runs to terminate at each evaluation interval. An integer value between 1 and 99.
 * `evaluation_interval`: (optional) the frequency for applying the policy
 * `delay_evaluation`: (optional) delays the first policy evaluation for a specified number of intervals
-* `exclude_finished_jobs`: (optional) specifies whether to exclude finished jobs as part of the early termination policy
+* `exclude_finished_jobs`: specifies whether to exclude finished jobs when applying the policy
 
 
 ```Python
 from azureml.train.hyperdrive import TruncationSelectionPolicy
-early_termination_policy = TruncationSelectionPolicy(evaluation_interval=1, truncation_percentage=20, delay_evaluation=5)
+early_termination_policy = TruncationSelectionPolicy(evaluation_interval=1, truncation_percentage=20, delay_evaluation=5, exclude_finished_jobs=true)
 ```
 
-In this example, the early termination policy is applied at every interval starting at evaluation interval 5. A run terminates at interval 5 if its performance at interval 5 is in the lowest 20% of performance of all runs at interval 5.
+In this example, the early termination policy is applied at every interval starting at evaluation interval 5. A run terminates at interval 5 if its performance at interval 5 is in the lowest 20% of performance of all runs at interval 5 and will exclude finished jobs when applying the policy.
 
 ### No termination policy (default)