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@@ -20,7 +20,8 @@ experimental[]
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[[ml-put-dfanalytics-prereq]]
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==== {api-prereq-title}
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-If the {es} {security-features} are enabled, you must have the following built-in roles and privileges:
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+If the {es} {security-features} are enabled, you must have the following
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+built-in roles and privileges:
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* `machine_learning_admin`
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* `kibana_user` (UI only)
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@@ -66,12 +67,11 @@ upper bound on the improvement in validation loss.
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A fixed number of rounds is used for optimization which depends on the number of
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parameters being optimized. The optimization starts with random search, then
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Bayesian optimization is performed that is targeting maximum expected
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-improvement. If you override any parameters,
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-//TBD: What is meant by overriding them? Explicitly setting the parameter instead of letting it take the default?
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-the optimization calculates the value of the remaining parameters accordingly
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-and uses the value you provided for the overridden parameter. The number of
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-rounds are reduced respectively. The validation error is estimated in each round
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-by using 4-fold cross validation.
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+improvement. If you override any parameters by explicitely setting it, the
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+optimization calculates the value of the remaining parameters accordingly and
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+uses the value you provided for the overridden parameter. The number of rounds
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+are reduced respectively. The validation error is estimated in each round by
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+using 4-fold cross validation.
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[[ml-put-dfanalytics-path-params]]
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==== {api-path-parms-title}
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@@ -104,7 +104,6 @@ TIP: Advanced parameters are for fine-tuning {classanalysis}. They are set
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automatically by <<ml-hyperparam-optimization,hyperparameter optimization>>
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to give minimum validation error. It is highly recommended to use the default
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values unless you fully understand the function of these parameters.
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-
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--
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`analysis`.`classification`.`dependent_variable`::::
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