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 | |
Int16 | https://msdn.microsoft.com/library/system.int16(v=vs.110).aspx |
Int32 | |
Int64 | |
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[] | |
Guid | GUIDs are converted to strings on input |
In addition, Machine Learning Studio supports the following custom data classes.
Data Type | Description |
|---|---|
The DataTable interface defines the structure of all datasets used in Azure Machine Learning. | |
The ICluster interface defines the structure of clustering models. | |
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. | |
The ILearner interface provides a generic structure for defining and saving analytical models, excluding some special types such as clustering models. | |
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. |