An iterative feature selection method that internally utilizes varius Machine Learning methods that have embeded feature reduction in order to shrink down the feature space into a small and yet robust set.
From time to time users are facing some errors but due to the sensitivity/confidentiality of the data, they cannot provide the input data so that I can track the issue.
The proposal
It would be convenient to have a debug argument that stores:
many info regarding the processes in a log file
store the output of every step in a RDS file so that we can have a better view on output of each step without needing to run the whole data
The 2nd item can then be later anonymized (feature names can be removed, possible residue of actual data should be stripped, and etc.) so that it make it safe to share without being concerned about the confidentiality of the data.
The problem
From time to time users are facing some errors but due to the sensitivity/confidentiality of the data, they cannot provide the input data so that I can track the issue.
The proposal
It would be convenient to have a
debugargument that stores:RDSfile so that we can have a better view on output of each step without needing to run the whole dataThe 2nd item can then be later anonymized (feature names can be removed, possible residue of actual data should be stripped, and etc.) so that it make it safe to share without being concerned about the confidentiality of the data.