This lays out how to process categorical data for CASTLE
Motivation
Come up with easy way to turn categorical data into data which can be used for CASTLE so it can be used for Machine Learning afterwards.
Design
process returns a Pandas DataFrame with 2 arguments and is included in the ml_utilities module. The arguments are:
data: The DataFrame that is to be processed
category: A dict containing the hierarchies of categorical data. Each key is a column in data and has the hierarchy of its values.
Output Format
The return value is a mutated version of data which contains the same column headers, but categorical values converted to numbers based on their position in the hierarchy.
Summary
This lays out how to process categorical data for CASTLE
Motivation
Come up with easy way to turn categorical data into data which can be used for CASTLE so it can be used for Machine Learning afterwards.
Design
processreturns a PandasDataFramewith 2 arguments and is included in theml_utilitiesmodule. The arguments are:data: TheDataFramethat is to be processedcategory: A dict containing the hierarchies of categorical data. Each key is a column indataand has the hierarchy of its values.Output Format
The return value is a mutated version of
datawhich contains the same column headers, but categorical values converted to numbers based on their position in the hierarchy.Drawbacks
None