Update how-to-configure-auto-train.md

Author: cartacioS

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

@@ -169,9 +169,7 @@ Some examples include:
 
 The three different `task` parameter values (the third task-type is `forecasting`, and uses a similar algorithm pool as `regression` tasks) determine the list of models to apply. Use the `whitelist` or `blacklist` parameters to further modify iterations with the available models to include or exclude. The list of supported models can be found on [SupportedModels Class](https://docs.microsoft.com/python/api/azureml-train-automl-client/azureml.train.automl.constants.supportedmodels) for ([Classification](https://docs.microsoft.com/python/api/azureml-train-automl-client/azureml.train.automl.constants.supportedmodels.classification), [Forecasting](https://docs.microsoft.com/python/api/azureml-train-automl-client/azureml.train.automl.constants.supportedmodels.forecasting), and [Regression](https://docs.microsoft.com/python/api/azureml-train-automl-client/azureml.train.automl.constants.supportedmodels.regression)).
 
-Automated ML's validation serivce will reduce the `experiment_timeout_minutes` based on row count in order to help avoid experiment timeout failure. The minimum timeout is 15 minutes, and the following rules may apply to your experiment depending on your data.
-* If your data contains 1,000 or more rows, then `experiment_timeout_minutes` will default to 30 minutes.
-* If your data contains 10,000 or more rows, then `experiment_timeout_minutes` will default to 60 minutes.
+Automated ML's validation serivce will reduce the `experiment_timeout_minutes` will require a minimum timeout of 15 minutes in order to help avoid experiment timeout failures.
 
 ### Primary Metric
 The primary metric determines the metric to be used during model training for optimization. The available metrics you can select is determined by the task type you choose, and the following table shows valid primary metrics for each task type.