Merge pull request #57581 from garyericson/11-08-studio-selectors-3

Removed selector that was empty from ML Studio articles

Author: Ja-Dunn

articles/machine-learning/team-data-science-process/spark-advanced-data-exploration-modeling.md

@@ -19,7 +19,6 @@ ms.author: deguhath
 
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 # Advanced data exploration and modeling with Spark
-[!INCLUDE [machine-learning-spark-modeling](../../../includes/machine-learning-spark-modeling.md)]
 
 This walkthrough uses HDInsight Spark to do data exploration and train binary classification and regression models using cross-validation and hyperparameter optimization on a sample of the NYC taxi trip and fare 2013 dataset. It walks you through the steps of the [Data Science Process](https://aka.ms/datascienceprocess), end-to-end, using an HDInsight Spark cluster for processing and Azure blobs to store the data and the models. The process explores and visualizes data brought in from an Azure Storage Blob and then prepares the data to build predictive models. Python has been used to code the solution and to show the relevant plots. These models are build using the Spark MLlib toolkit to do binary classification and regression modeling tasks. 
 

articles/machine-learning/team-data-science-process/spark-data-exploration-modeling.md

@@ -19,7 +19,6 @@ ms.author: deguhath
 
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 # Data exploration and modeling with Spark
-[!INCLUDE [machine-learning-spark-modeling](../../../includes/machine-learning-spark-modeling.md)]
 
 This walkthrough uses HDInsight Spark to do data exploration and binary classification and regression modeling tasks on a sample of the NYC taxi trip and fare 2013 dataset.  It walks you through the steps of the [Data Science Process](https://aka.ms/datascienceprocess), end-to-end, using an HDInsight Spark cluster for processing and Azure blobs to store the data and the models. The process explores and visualizes data brought in from an Azure Storage Blob and then prepares the data to build predictive models. These models are build using the Spark MLlib toolkit to do binary classification and regression modeling tasks.
 

articles/machine-learning/team-data-science-process/spark-model-consumption.md

@@ -19,7 +19,6 @@ ms.author: deguhath
 
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 # Operationalize Spark-built machine learning models
-[!INCLUDE [machine-learning-spark-modeling](../../../includes/machine-learning-spark-modeling.md)]
 
 This topic shows how to operationalize a saved machine learning model (ML) using Python on HDInsight Spark clusters. It describes how to load machine learning models that have been built using Spark MLlib and stored in Azure Blob Storage (WASB), and how to score them with datasets that have also been stored in WASB. It shows how to pre-process the input data, transform features using the indexing and encoding functions in the MLlib toolkit, and how to create a labeled point data object that can be used as input for scoring with the ML models. The models used for scoring include Linear Regression, Logistic Regression, Random Forest Models, and Gradient Boosting Tree Models.
 

articles/machine-learning/team-data-science-process/spark-overview.md

@@ -20,7 +20,6 @@ ms.author: deguhath
 
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 # Overview of data science using Spark on Azure HDInsight
-[!INCLUDE [machine-learning-spark-modeling](../../../includes/machine-learning-spark-modeling.md)]
 
 This suite of topics shows how to use HDInsight Spark to complete common data science tasks such as data ingestion, feature engineering, modeling, and model evaluation. The data used is a sample of the 2013 NYC taxi trip and fare dataset. The models built include logistic and linear regression, random forests, and gradient boosted trees. The topics also show how to store these models in Azure blob storage (WASB) and how to score and evaluate their predictive performance. More advanced topics cover how models can be trained using cross-validation and hyper-parameter sweeping. This overview topic also references the topics that describe how to set up the Spark cluster that you need to complete the steps in the walkthroughs provided.