note

Author: nibaccam

articles/machine-learning/how-to-configure-auto-train.md

@@ -251,9 +251,12 @@ Ensemble models are enabled by default, and appear as the final run iterations i
 
 There are multiple default arguments that can be provided as `kwargs` in an `AutoMLConfig` object to alter the default ensemble behavior.
 
-* `ensemble_download_models_timeout_sec`: During VotingEnsemble and StackEnsemble model generation, multiple fitted models from the previous child runs are downloaded. If you encounter this error: `AutoMLEnsembleException: Could not find any models for running ensembling`, then you may need to provide more time for the models to be downloaded. The default value is 300 seconds for downloading these models in parallel and there is no maximum timeout limit. Configure this parameter with a higher value than 300 secs, if more time is needed. **Note**: If the timeout is reached and there are models downloaded, then the ensembling proceeds with as many models it has downloaded (it's not required that all the models need to be downloaded to finish within that timeout).
+* `ensemble_download_models_timeout_sec`: During **VotingEnsemble** and **StackEnsemble** model generation, multiple fitted models from the previous child runs are downloaded. If you encounter this error: `AutoMLEnsembleException: Could not find any models for running ensembling`, then you may need to provide more time for the models to be downloaded. The default value is 300 seconds for downloading these models in parallel and there is no maximum timeout limit. Configure this parameter with a higher value than 300 secs, if more time is needed. 
 
-The following parameters only apply to StackEnsemble models: 
+  > [!NOTE]
+  >  If the timeout is reached and there are models downloaded, then the ensembling proceeds with as many models it has downloaded. It's not required that all the models need to be downloaded to finish within that timeout.
+
+The following parameters only apply to **StackEnsemble** models: 
 
 * `stack_meta_learner_type`: the meta-learner is a model trained on the output of the individual heterogeneous models. Default meta-learners are `LogisticRegression` for classification tasks (or `LogisticRegressionCV` if cross-validation is enabled) and `ElasticNet` for regression/forecasting tasks (or `ElasticNetCV` if cross-validation is enabled). This parameter can be one of the following strings: `LogisticRegression`, `LogisticRegressionCV`, `LightGBMClassifier`, `ElasticNet`, `ElasticNetCV`, `LightGBMRegressor`, or `LinearRegression`.