Edit Metadata


Support for Machine Learning Studio (classic) will end on 31 August 2024. We recommend you transition to Azure Machine Learning by that date.

Beginning 1 December 2021, you will not be able to create new Machine Learning Studio (classic) resources. Through 31 August 2024, you can continue to use the existing Machine Learning Studio (classic) resources.

ML Studio (classic) documentation is being retired and may not be updated in the future.

Edits metadata associated with columns in a dataset

Category: Data Transformation / Manipulation


Applies to: Machine Learning Studio (classic) only

Similar drag-and-drop modules are available in Azure Machine Learning designer.

Module overview

This article describes how to use the Edit Metadata module in Machine Learning Studio (classic) to change metadata that is associated with columns in a dataset. The values and the data types in the dataset are not actually altered; what changes is the metadata inside Machine Learning that tells downstream components how to use the column.

Typical metadata changes might include:

  • Treating Boolean or numeric columns as categorical values

  • Indicating which column contains the class label, or the values you want to categorize or predict

  • Marking columns as features

  • Changing date/time values to a numeric value, or vice versa

  • Renaming columns

Use Edit Metadata any time you need to modify the definition of a column, typically to meet requirements for a downstream module. For example, some modules can 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.

How to configure Edit Metadata

  1. In Machine Learning Studio (classic), add Edit Metadata module to your experiment and connect the dataset you want to update. You can find it under Data Transformation, in the Manipulate category.

  2. 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.


    Need help using column indices? See the Technical Notes section.

  3. Select the Data type option if you need to assign a different data type to the selected columns. Changing the data type might be needed 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, Floating point, Boolean, DateTime, and TimeSpan.

    • If multiple columns are selected, you must apply the metadata changes to all selected columns. For example, let's say you choose 2-3 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 do not specify a new data type, the column metadata is unchanged.

    • Changes of data type affect only the metadata that is associated with the dataset and how the data is handled in downstream operations. The actual column values are not altered unless you perform a different operation (such as rounding) on the column. You can recover the original data type at any time by using Edit Metadata to reset the column data type.


    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.

Machine Learning can convert dates to numbers, or numbers to dates, if the numbers are compatible with one of the supported .NET DateTime objects. For more information, see the Technical Notes section.

  1. Select the Categorical option to specify that the values in the selected columns should be treated as categories.

    For example, you might have a column that contains the numbers 0,1 and 2, but know that the numbers actually mean "Smoker", "Non smoker" and "Unknown". In that case, by flagging the column as categorical you can ensure that the values are not used in numeric calculations, only to group data.

  2. Use the Fields option if you want to change the way that Machine Learning uses the data in a model.

    • Feature: Use this option to flag a column as a feature, for use with modules that operate only on feature columns. By default, all columns are initially treated as features.

    • Label: Use this option to mark the label (also known as the predictable attribute, or target variable). Many modules requires that at least one (and only one) label column be present in the dataset.

      In many cases, Machine Learning can infer that a column contains a class label, but by setting this metadata you can ensure that the column is identified correctly. Setting this option does not change data values, only the way that some machine learning algorithms handle the data.

    • Weight: Use this option with numeric data to indicate that column values represents weights for use in machine learning scoring or training operations. Only one weight column can be present in a dataset, and the column must be numeric. This option works only in these models: Two-Class Logistic Regression, Two-Class Support Vector Machine, and Two-Class Neural Network.


    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 IDs can cause problems when used in a model.

    Fortunately "under the covers" Machine Learning keeps all your data, so 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 module. Later you can merge the columns back into the dataset by using the Add Columns module.

  3. Use the following options to clear previous selections and restore metadata to the default values.

    • Clear feature: Use this option to remove the feature flag.

      Because all columns are initially treated as features, for modules that perform mathematical operations, you might need to use this option to prevent numeric columns from being treated as variables.

    • Clear label: Use this option to remove the label metadata from the specified column.

    • Clear score: Use this option to remove the score metadata from the specified column.

      Currently the ability to explicitly mark a column as a score is not available in Machine Learning. However, some operations result in a column being flagged as a score internally. Also, a custom R module might output score values.

    • Clear weight: Use this option to remove the weight metadata from the specified column.

  4. For New column names, type the new name of the selected column or columns.

    • Column names can use only characters that are supported by the UTF-8 encoding. Empty strings, nulls, or names consisting entirely of spaces are not allowed.

    • To rename multiple columns, type the names as a comma-separated list in order of the column indices.

    • All selected columns must be renamed. You cannot omit or skip columns.


    If you need to rename multiple columns, you can paste in a comma-delimited string prepared in advance. Or, use the Execute R Script or Apply SQL Transformation modules. See the Technical Notes section for code and examples.

  5. Run the experiment.


For examples of how Edit Metadata is used in preparing data and building models, see the Azure AI Gallery:

  • Breast cancer detection: Column names are changed after joining to datasets. The Patient ID column is also flagged as categorical to ensure that it is not used in a calculation, but rather than handled as a string value.

  • Twitter sentiment analysis: Demonstrates how to use Edit Metadata to ensure that columns are treated as features. Later in the experiment, the feature metadata is cleared.

  • Data Processing and analysis: In this sample, Edit Metadata is used to define new column names for data that was loaded from a webpage.

Technical notes

This section contains known issues, frequently asked questions, and some examples of common workarounds.

Known Issues

  • Custom metadata is not supported. It is not possible to use custom metadata in Machine Learning or to edit column metadata outside Edit Metadata. For example, you cannot add metadata indicating that a column is a unique identifier, or add other descriptive attributes . Machine Learning supports only the metadata attributes that are used within R for working with factors, features, weights, and labels.

  • Unsupported data types. The following numeric data types are not supported: Double (decimal) and TimeStamp.

  • Identifying score columns. Currently there is no option in Edit Metadata to flag a column as containing scores. However, you can use the Execute R Script module with a script similar to the following to indicate that a column contains scores:

    dataset <- maml.mapInputPort(1)   
    attr(dataset$x, "label.type")= "True Labels"  
    attr(dataset$y, "feature.channel")= "Multiclass Classification Scores"  
    attr(dataset$y, "score.type")= "Assigned Labels"  
  • Problems with datetime formats. The underlying datetime data type used by Machine Learning is POSIXct.

    If all dates in a column can be parsed by the default parser, the column is imported and treated as string data.

    If you try to convert a column to DateTime by using the Edit Metadata module and get an error, it means that the date is not in a format that .Net accepts by default. In this case, we recommend that you use the Execute R Script module or the Apply SQL Transformation module to transform your column to a format that is accepted by the default parser.

    DateTime.Parse Method

    Standard Date and Time Format Strings

Selecting columns using column indices

In very large datasets, it is not feasible to manually type or select all column names. Using the column index is one shortcut that you can use to specify many columns. This section provides some tips on using column indices.

For example, open the Column Selector, click WITH RULES, select Include and column indices, and then type a range or series of numbers as follows:

  • Type 1-20 to select the first 20 columns
  • Type 5-20 to select a range of columns beginning at 5 and including column 20.
  • Type 1,5,10,15 to select discontinuous columns
  • Type 1-2, 5 to select columns 1, 2 and 5, skipping columns 3 and 4
  • You cannot type an index value that is greater than the number of columns available in the dataset.

The following experiments provide some examples of other methods for selecting and modifying multiple columns:

  • Binary Classification: Breast Cancer Detection: The original data contained many blank columns generated during import from a spreadsheet. The extra columns were removed by specifying columns 1-11 in the Split Data module.

  • Download dataset from UCI: Demonstrates how you can provide column names as a list using the Enter Data Manually module, and then insert the list into the dataset as headings, using the Execute R script module.

  • Regex Select Columns: This experiment provides a custom module that lets you apply a regular expression to column names. You could use this module as an input to Edit Metadata.

Alternate methods for modifying column names

If you have many columns to rename, you can use the Execute R Script module, or the Apply SQL Transformation module.

Using R script

Data sets used by Machine Learning are passed into this module as a data.frame, meaning that you can use the R colnames() function and other related R functions, to list or change column names.

For example, the following code creates a list of new column names, and then applies that list to the input dataset to generate new column headings.

irisdata <- maml.mapInputPort(1);    
colnames(irisdata) = newnames

The following example uses a regular expression in R to globally replace all instances of the specified string in the column names for irisdata:

# Map input dataset to variable
newirisdata <- maml.mapInputPort(1) # class: data.frame
names(newirisdata) <- gsub("col", "iris", names(newirisdata))

Using SQL

The following example takes a dataset as the input, and then changes the column names using the AS keyword.

SELECT col1 as [C1], 
  col2 as [C2], 
  col3 as [C3], 
  col4 as [C4],
  col5 as [C5] 
FROM t1;

Expected input

Name Type Description
Dataset Data Table Input dataset

Module parameters

Name Range Type Default Description
Column Any ColumnSelection Choose the columns to which your changes should apply.
Data type List Metadata editor datatype Unchanged Specify the new data type for the column.
Categorical List Metadata editor categorical Unchanged Indicate if the column should be flagged as categorical.
Fields List Metadata editor flag Unchanged Specify if the column should be considered a feature or label by learning algorithms.
New column names any String Type the new names for the columns.


Name Type Description
Results dataset Data Table Dataset with changed metadata


Exception Description
Error 0003 An exception occurs if one or more of input datasets are null or empty.
Error 0017 An exception occurs if one or more specified columns have a type that is unsupported by the current module.
Error 0020 An exception occurs if the number of columns in some of the datasets that are passed to the module is too small.
Error 0031 An exception occurs if the number of columns in the column set is less than needed.
Error 0027 An exception occurs when two objects have to be of the same size, but they are not.
Error 0028 An exception occurs when the column set contains duplicate column names and it is not allowed.
Error 0037 An exception occurs if multiple label columns are specified and only one is allowed.

For a list of errors specific to Studio (classic) modules, see Machine Learning Error codes.

For a list of API exceptions, see Machine Learning REST API Error Codes.

See also

Data Transformation
A-Z Module List