ML Studio (classic) Module Data Types

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Important

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.

This article describes the .NET data types that are supported in Machine Learning Studio (classic) for external data. It also describes the custom data type classes that are used for passing data between modules within an experiment.

Table of .NET data types

The following .NET types are supported by Machine Learning Studio (classic) modules.

.NET Data Type Comments
Boolean https://msdn.microsoft.com/library/wts33hb3.aspx
Int16 https://msdn.microsoft.com/library/system.int16(v=vs.110).aspx
Int32 https://msdn.microsoft.com/library/06bkb8w2.aspx
Int64 https://msdn.microsoft.com/library/system.int64.aspx
Single https://msdn.microsoft.com/library/system.single(v=vs.110).aspx
Double https://msdn.microsoft.com/library/system.double(v=vs.110).aspx
String https://msdn.microsoft.com/library/system.string(v=vs.110).aspx
datetime https://msdn.microsoft.com/library/system.datetime(v=vs.110).aspx
DateTimeOffset https://msdn.microsoft.com/library/system.datetimeoffset(v=vs.110).aspx
TimeSpan https://msdn.microsoft.com/library/system.timespan(v=vs.110).aspx
Byte https://msdn.microsoft.com/library/system.byte(v=vs.110).aspx
Byte[] https://msdn.microsoft.com/library/system.byte.aspx
Guid GUIDs are converted to strings on input

Table of custom data types

In addition, Machine Learning Studio (classic) supports the following custom data classes.

Data Type Description
Data Table The DataTable interface defines the structure of all datasets used in Machine Learning.
ICluster interface The ICluster interface defines the structure of clustering models.
IFilter interface The IFilter interface defines the structure of digital signal processing filters applied to an entire series of numerical values. Filters can be created and then saved and applied to a new series.
ILearner interface The ILearner interface provides a generic structure for defining and saving analytical models, excluding some special types such as clustering models.
ITransform interface The ITransform interface provides a generic structure for defining and saving transformations. You can create an iTransform using Machine Learning Studio (classic) and then apply the transformation to new datasets.

See also

Machine Learning Studio (classic)