Update articles/machine-learning/how-to-use-automated-ml-for-ml-models.md

Author: Jak-MS

articles/machine-learning/how-to-use-automated-ml-for-ml-models.md

@@ -123,7 +123,7 @@ Otherwise, you see a list of your recent automated  ML experiments, including th
     Additional configurations|Description
     ------|------
     Primary metric| Main metric used for scoring your model. [Learn more about model metrics](how-to-configure-auto-train.md#primary-metric).
-    Enable ensemble stacking | Ensemble learning improves machine learning results and predictive performance by combining multiple models as opposed to using single models. [Learn more about ensemble models](concept-automated-ml.md#-ensemble-models).
+    Enable ensemble stacking | Ensemble learning improves machine learning results and predictive performance by combining multiple models as opposed to using single models. [Learn more about ensemble models](concept-automated-ml.md#ensemble).
     Blocked algorithm| Select algorithms you want to exclude from the training job. <br><br> Allowing algorithms is only available for [SDK experiments](how-to-configure-auto-train.md#supported-algorithms). <br> See the [supported algorithms for each task type](/python/api/azureml-automl-core/azureml.automl.core.shared.constants.supportedmodels).
     Exit criterion| When any of these criteria are met, the training job is stopped. <br> *Training job time (hours)*: How long to allow the training job to run. <br> *Metric score threshold*:  Minimum metric score for all pipelines. This ensures that if you have a defined target metric you want to reach, you don't spend more time on the training job than necessary.
     Concurrency| *Max concurrent iterations*: Maximum number of pipelines (iterations) to test in the training job. The job won't run more than the specified number of iterations. Learn more about how automated ML performs [multiple child jobs on clusters](how-to-configure-auto-train.md#multiple-child-runs-on-clusters).