I am working with a dataset that has 30,000 features most of them are logocal datatypes.
I wish that when this data is imported to workspace an appropriate optimal datatypes is assiged automatically to each column(attributes).
I observe through the data cleaning tool that they are not being assigned the optimal datatypes
such as logical datatypes are assigned double( wasting alot of space).
Manualy assigning will be very tedious task because of 30 thousand features.
Moreover, other algorithms are demanding too much memory because of this such as feature ranking methods and machine learning algorithms
Th system i am using has 32GB of RAM.I am working with a dataset that has 30,000 features most of them are logocal datatypes.
I wish that when this data is imported to workspace an appropriate optimal datatypes is assiged automatically to each column(attributes).
I observe through the data cleaning tool that they are not being assigned the optimal datatypes
such as logical datatypes are assigned double( wasting alot of space).
Manualy assigning will be very tedious task because of 30 thousand features.
Moreover, other algorithms are demanding too much memory because of this such as feature ranking methods and machine learning algorithms
Th system i am using has 32GB of RAM. I am working with a dataset that has 30,000 features most of them are logocal datatypes.
I wish that when this data is imported to workspace an appropriate optimal datatypes is assiged automatically to each column(attributes).
I observe through the data cleaning tool that they are not being assigned the optimal datatypes
such as logical datatypes are assigned double( wasting alot of space).
Manualy assigning will be very tedious task because of 30 thousand features.
Moreover, other algorithms are demanding too much memory because of this such as feature ranking methods and machine learning algorithms
Th system i am using has 32GB of RAM. high dimansional data, optimal datatypes casting, importing high dimensional data MATLAB Answers — New Questions

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