edit pass: edit-metadata

Author: BobbySchmidt2

articles/machine-learning/algorithm-module-reference/edit-metadata.md

@@ -1,7 +1,7 @@
 ---
-title:  "Edit Metadata: Module Reference"
+title:  "Edit Metadata: Module reference"
 titleSuffix: Azure Machine Learning service
-description: Learn how to use the Edit Metadata module in Azure Machine Learning service to change metadata that is associated with columns in a dataset.
+description: Learn how to use the Edit Metadata module in the Azure Machine Learning service to change metadata that's associated with columns in a dataset.
 services: machine-learning
 ms.service: machine-learning
 ms.subservice: core
@@ -16,42 +16,42 @@ ROBOTS: NOINDEX
 
 This article describes a module of the visual interface (preview) for the Azure Machine Learning service.
 
-Use this module to change metadata that is associated with columns in a dataset. The value and data type of the dataset will be changed after use of the Edit Metadata module.
+Use the Edit Data module to change metadata that's associated with columns in a dataset. The value and data type of the dataset will  change after use of the Edit Metadata module.
 
 Typical metadata changes might include:
 
-+ Treating Boolean or numeric columns as categorical values  
++ Treating Boolean or numeric columns as categorical values.
 
-+ Indicating which column contains the **class** label or contains the values you want to categorize or predict  
++ Indicating which column contains the **class** label or contains the values you want to categorize or predict.
 
-+ Marking columns as features
++ Marking columns as features.
 
-+ Changing date/time values to numeric values or vice versa  
++ Changing date/time values to numeric values or vice versa.
 
-+ Renaming columns
++ Renaming columns.
 
- Use Edit Metadata anytime you need to modify the definition of a column, typically to meet requirements for a downstream module. For example, some modules work only with specific data types or require flags on the columns, such as **IsFeature** or **IsCategorical**.  
+ Use Edit Metadata anytime you need to modify the definition of a column, typically to meet requirements for a downstream module. For example, some modules work only with specific data types or require flags on the columns, such as `IsFeature` or `IsCategorical`.  
 
- After performing the required operation, you can reset the metadata to its original state.
+ After you perform the required operation, you can reset the metadata to its original state.
 
 ## Configure Edit Metadata
 
 1. In Azure Machine Learning, add the Edit Metadata module to your experiment and connect the dataset you want to update. You can find the dataset under **Data Transformation** in the **Manipulate** category.
 
-1. Click **Launch the column selector** and choose the column or set of columns to work with. You can choose columns individually by name or index, or you can choose a group of columns by type.  
+1. Select **Launch the column selector** and choose the column or set of columns to work with. You can choose columns individually by name or index, or you can choose a group of columns by type.  
 
 1. Select the **Data type** option if you need to assign a different data type to the selected columns. You might need to change the data type for certain operations. For example, if your source dataset has numbers handled as text, you must change them to a numeric data type before using math operations.
 
-    + The data types supported are **String**, **Integer**, **Double**, **Boolean**, and **DateTime**.
+    + The supported data types are **String**, **Integer**, **Double**, **Boolean**, and **DateTime**.
 
-    + If you select multiple columns, you must apply the metadata changes to *all* selected columns. For example, let's say you choose two or three numeric columns. You could change them all to a string data type and rename them in one operation. However, you can't change one column to a string data type and another column from a float to an integer.
+    + If you select multiple columns, you must apply the metadata changes to *all* selected columns. For example, let's say you choose two or three numeric columns. You can change them all to a string data type and rename them in one operation. However, you can't change one column to a string data type and another column from a float to an integer.
 
-    + If you do not specify a new data type, the column metadata is unchanged.
+    + If you don't specify a new data type, the column metadata is unchanged.
 
     + The column type and values will change after you perform the Edit Metadata operation. You can recover the original data type at any time by using Edit Metadata to reset the column data type.  
 
     > [!NOTE]
-    > If you change any type of number to the **DateTime** type, leave the **DateTime Format** field blank. Currently it is not possible to specify the target data format.  
+    > If you change any type of number to the **DateTime** type, leave the **DateTime Format** field blank. Currently it isn't possible to specify the target data format.  
 
 1. Select the **Categorical** option to specify that the values in the selected columns should be treated as categories.
 
@@ -61,14 +61,14 @@ Typical metadata changes might include:
 
     + **Feature**: Use this option to flag a column as a feature in modules that operate only on feature columns. By default, all columns are initially treated as features.  
 
-    + **Label**: Use this option to mark the label, which is also known as the predictable attribute or target variable. Many modules require that exactly one label column be present in the dataset.
+    + **Label**: Use this option to mark the label, which is also known as the predictable attribute or target variable. Many modules require that exactly one label column is present in the dataset.
 
-        In many cases, Azure Machine Learning can infer that a column contains a class label. By setting this metadata you can ensure that the column is identified correctly. Setting this option does not change data values. It changes only the way that some machine-learning algorithms handle the data.
+        In many cases, Azure Machine Learning can infer that a column contains a class label. By setting this metadata, you can ensure that the column is identified correctly. Setting this option does not change data values. It changes only the way that some machine-learning algorithms handle the data.
 
     > [!TIP]
-    > Do you have data that doesn't fit into these categories? For example, your dataset might contain values such as unique identifiers that are not useful as variables. Sometimes such IDs can cause problems when used in a model.
+    > Do you have data that doesn't fit into these categories? For example, your dataset might contain values such as unique identifiers that aren't useful as variables. Sometimes such IDs can cause problems when used in a model.
     >
-    > Fortunately, under the covers Azure Machine Learning keeps all of your data, so that you don't have to delete such columns from the dataset. When you need to perform operations on some special set of columns, just remove all other columns temporarily by using the [Select Columns in Dataset](select-columns-in-dataset.md) module. Later you can merge the columns back into the dataset by using the [Add Columns](add-columns.md) module.  
+    > Fortunately, Azure Machine Learning keeps all of your data, so that you don't have to delete such columns from the dataset. When you need to perform operations on some special set of columns, just remove all other columns temporarily by using the [Select Columns in Dataset](select-columns-in-dataset.md) module. Later you can merge the columns back into the dataset by using the [Add Columns](add-columns.md) module.  
   
 1. Use the following options to clear previous selections and restore metadata to the default values.  
 
@@ -80,18 +80,18 @@ Typical metadata changes might include:
 
     + **Clear score**: Use this option to remove the **score** metadata from the specified column.  
 
-         You currently cannot explicitly mark a column as a score in Azure Machine Learning. However, some operations result in a column being flagged as a score internally. Also, a custom R module might output score values.
+         You currently can't explicitly mark a column as a score in Azure Machine Learning. However, some operations result in a column being flagged as a score internally. Also, a custom R module might output score values.
 
-1. For **New column names**, type the new name of the selected column or columns.  
+1. For **New column names**, enter the new name of the selected column or columns.  
 
-    + Column names can use only characters that are supported by UTF-8 encoding. Empty strings, nulls, or names consisting entirely of spaces are not allowed.  
+    + Column names can use only characters that are supported by UTF-8 encoding. Empty strings, nulls, or names that consist entirely of spaces aren't allowed.  
 
-    + To rename multiple columns, type the names as a comma-separated list in order of the column indices.  
+    + To rename multiple columns, enter the names as a comma-separated list in order of the column indexes.  
 
-    + All selected columns must be renamed. You cannot omit or skip columns.  
+    + All selected columns must be renamed. You can't omit or skip columns.  
 
 1. Run the experiment.  
 
 ## Next steps
 
-See the [set of modules available](module-reference.md) to Azure Machine Learning service.
+See the [set of modules available](module-reference.md) to the Azure Machine Learning service.