Merge pull request #114489 from nibaccam/concept-automl

AutoML | Concept page: remove duplicate sections

Author: PRMerger17

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

@@ -130,28 +130,7 @@ 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). 
 
-## Classification & regression
 
-Classification and regression are the most common types of machine learning tasks. Both are types of supervised learning in which models learn using training data, and apply those learnings to new data. Azure Machine Learning offers featurizations specifically for these tasks, such as deep neural network text featurizers for classification. Learn more about [featurization options](how-to-use-automated-ml-for-ml-models.md#featurization). 
-
-The main goal of classification models is to predict which categories new data will fall into based on learnings from its training data. Common classification examples include fraud detection, handwriting recognition, and object detection.  Learn more and see an example of [classification with automated machine learning](tutorial-train-models-with-aml.md).
-
-Different from classification where predicted output values are categorical, regression models predict numerical output values based on independent predictors. In regression, the objective is to help establish the relationship among those independent predictor variables by estimating how one variable impacts the others. For example, automobile price based on features like, gas mileage, safety rating, etc. Learn more and see an example of [regression with automated machine learning](tutorial-auto-train-models.md).
-
-## Time-series forecasting
-
-Building forecasts is an integral part of any business, whether it's revenue, inventory, sales, or customer demand. You can use automated ML to combine techniques and approaches and get a recommended, high-quality time-series forecast.
-
-An automated time-series experiment is treated as a multivariate regression problem. Past time-series values are "pivoted" to become additional dimensions for the regressor together with other predictors. This approach, unlike classical time series methods, has an advantage of naturally incorporating multiple contextual variables and their relationship to one another during training. Automated ML learns a single, but often internally branched model for all items in the dataset and prediction horizons. More data is thus available to estimate model parameters and generalization to unseen series becomes possible.
-
-Learn more and see an example of [automated machine learning for time series forecasting](how-to-auto-train-forecast.md). Or, see the [energy demand notebook](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/automated-machine-learning/forecasting-energy-demand/auto-ml-forecasting-energy-demand.ipynb) for detailed code examples of advanced forecasting configuration including:
-
-* holiday detection and featurization
-* time-series and DNN learners (Auto-ARIMA, Prophet, ForecastTCN)
-* many models support through grouping
-* rolling-origin cross validation
-* configurable lags
-* rolling window aggregate features
 
 ## <a name="ensemble"></a> Ensemble models
 
@@ -164,14 +143,6 @@ The [Caruana ensemble selection algorithm](http://www.niculescu-mizil.org/papers
 
 See the [how-to](how-to-configure-auto-train.md#ensemble) for changing default ensemble settings in automated machine learning.
 
-## Use with 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="local-remote"></a>Guidance on local vs. remote managed ML compute targets
 
 The web interface for automated ML always uses a remote [compute target](concept-compute-target.md).  But when you use the Python SDK, you will choose either a local compute or a remote compute target for automated ML training.
@@ -271,17 +242,6 @@ 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
@@ -292,24 +252,21 @@ See how to convert to ONNX format [in this Jupyter notebook example](https://git
 
 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:
 
-+ Follow the [Tutorial: Automatically train a regression model with Azure Machine Learning](tutorial-auto-train-models.md) 
-
-+ Learn how to use a [remote compute target](how-to-auto-train-remote.md)
-
 + Configure the settings for automatic training experiment:
   + In Azure Machine Learning studio, [use these steps](how-to-use-automated-ml-for-ml-models.md).
   + With the Python SDK, [use these steps](how-to-configure-auto-train.md).
 
++ Learn how to use a [remote compute target](how-to-auto-train-remote.md)
+
++ Follow the [Tutorial: Automatically train a regression model with Azure Machine Learning](tutorial-auto-train-models.md) 
+
 + Learn how to auto train using time series data, [use these steps](how-to-auto-train-forecast.md).
 
 + Try out [Jupyter Notebook samples for automated machine learning](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/automated-machine-learning/)
-
 * Automated ML is also available in other Microsoft solutions such as, 
 [ML.NET](https://docs.microsoft.com/dotnet/machine-learning/automl-overview), 
 [HDInsight](../hdinsight/spark/apache-spark-run-machine-learning-automl.md), [Power BI](https://docs.microsoft.com/power-bi/service-machine-learning-automated) and [SQL Server](https://cloudblogs.microsoft.com/sqlserver/2019/01/09/how-to-automate-machine-learning-on-sql-server-2019-big-data-clusters/)