Machine Learning Module Data Types

 

Updated: June 9, 2015

Microsoft Azure Machine Learning Studio supports various .NET data types for external data, and several custom data type classes for communicating information between modules within an experiment.

The following .NET types are supported by Machine Learning Studio 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

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

Data Type

Description

Data Table

The DataTable interface defines the structure of all datasets used in Azure 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 and then apply the transformation to new datasets.

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