Update concept-automated-ml.md

Author: j-martens

articles/machine-learning/concept-automated-ml.md

@@ -130,33 +130,12 @@ Enable this setting with:
 
 + Python SDK: Specifying `"feauturization": 'auto' / 'off' / 'FeaturizationConfig'` for the [`AutoMLConfig` class](/python/api/azureml-train-automl-client/azureml.train.automl.automlconfig.automlconfig). 
 
-## <a name="ensemble"></a> Ensemble models
-
-Automated machine learning supports ensemble models, which are enabled by default. Ensemble learning improves machine learning results and predictive performance by combining multiple models as opposed to using single models. The ensemble iterations appear as the final iterations of your run. Automated machine learning uses both voting and stacking ensemble methods for combining models:
-
-* **Voting**: predicts based on the weighted average of predicted class probabilities (for classification tasks) or predicted regression targets (for regression tasks).
-* **Stacking**: stacking combines heterogenous models and trains a meta-model based on the output from the individual models. The current default meta-models are LogisticRegression for classification tasks and ElasticNet for regression/forecasting tasks.
-
-The [Caruana ensemble selection algorithm](http://www.niculescu-mizil.org/papers/shotgun.icml04.revised.rev2.pdf) with sorted ensemble initialization is used to decide which models to use within the ensemble. At a high level, this algorithm initializes the ensemble with up to five models with the best individual scores, and verifies that these models are within 5% threshold of the best score to avoid a poor initial ensemble. Then for each ensemble iteration, a new model is added to the existing ensemble and the resulting score is calculated. If a new model improved the existing ensemble score, the ensemble is updated to include the new model.
-
-See the [how-to](how-to-configure-auto-train.md#ensemble) for changing default ensemble settings in automated machine learning.
-
-<a name="use-with-onnx"></a>
-
-## AutoML & ONNX
-
-With Azure Machine Learning, you can use automated ML to build a Python model and have it converted to the ONNX format. Once the models are in the ONNX format, they can be run on a variety of platforms and devices. Learn more about [accelerating ML models with ONNX](concept-onnx.md).
-
-See how to convert to ONNX format [in this Jupyter notebook example](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/automated-machine-learning/classification-bank-marketing-all-features/auto-ml-classification-bank-marketing-all-features.ipynb). Learn which [algorithms are supported in ONNX](how-to-configure-auto-train.md#select-your-experiment-type).
-
-The ONNX runtime also supports C#, so you can use the model built automatically in your C# apps without any need for recoding or any of the network latencies that REST endpoints introduce. Learn more about [inferencing ONNX models with the ONNX runtime C# API](https://github.com/Microsoft/onnxruntime/blob/master/docs/CSharp_API.md). 
+<a name="parity"></a>
 
 ## The studio vs SDK
 
 Learn about the parity and differences between the high-level automated ML capabilities available through the Python SDK and the studio in Azure Machine Learning. 
 
-<a name="parity"></a>
-
 ### Experiment settings 
 
 The following settings allow you to configure your automated ML experiment. 
@@ -202,6 +181,29 @@ These settings allow you to review and control your experiment runs and its chil
 |Get guardrails| ✓|✓|
 |Pause & resume runs| ✓| |
 
+## <a name="ensemble"></a> Ensemble models
+
+Automated machine learning supports ensemble models, which are enabled by default. Ensemble learning improves machine learning results and predictive performance by combining multiple models as opposed to using single models. The ensemble iterations appear as the final iterations of your run. Automated machine learning uses both voting and stacking ensemble methods for combining models:
+
+* **Voting**: predicts based on the weighted average of predicted class probabilities (for classification tasks) or predicted regression targets (for regression tasks).
+* **Stacking**: stacking combines heterogenous models and trains a meta-model based on the output from the individual models. The current default meta-models are LogisticRegression for classification tasks and ElasticNet for regression/forecasting tasks.
+
+The [Caruana ensemble selection algorithm](http://www.niculescu-mizil.org/papers/shotgun.icml04.revised.rev2.pdf) with sorted ensemble initialization is used to decide which models to use within the ensemble. At a high level, this algorithm initializes the ensemble with up to five models with the best individual scores, and verifies that these models are within 5% threshold of the best score to avoid a poor initial ensemble. Then for each ensemble iteration, a new model is added to the existing ensemble and the resulting score is calculated. If a new model improved the existing ensemble score, the ensemble is updated to include the new model.
+
+See the [how-to](how-to-configure-auto-train.md#ensemble) for changing default ensemble settings in automated machine learning.
+
+<a name="use-with-onnx"></a>
+
+## AutoML & ONNX
+
+With Azure Machine Learning, you can use automated ML to build a Python model and have it converted to the ONNX format. Once the models are in the ONNX format, they can be run on a variety of platforms and devices. Learn more about [accelerating ML models with ONNX](concept-onnx.md).
+
+See how to convert to ONNX format [in this Jupyter notebook example](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/automated-machine-learning/classification-bank-marketing-all-features/auto-ml-classification-bank-marketing-all-features.ipynb). Learn which [algorithms are supported in ONNX](how-to-configure-auto-train.md#select-your-experiment-type).
+
+The ONNX runtime also supports C#, so you can use the model built automatically in your C# apps without any need for recoding or any of the network latencies that REST endpoints introduce. Learn more about [inferencing ONNX models with the ONNX runtime C# API](https://github.com/Microsoft/onnxruntime/blob/master/docs/CSharp_API.md). 
+
+
+
 ## Next steps
 
 See examples and learn how to build models using automated machine learning: