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  • esdeesde
  • machine-learning
  • MLAirMLAir
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  • #309
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Issue created Jun 15, 2021 by Vincent Gramlich@gramlich16 of 6 checklist items completed6/6 checklist items

Class-based Oversampling technique

Target

Implement a class-based Oversampling technique. Classes are defined by fixed ppb intervalls and the Oversampling then (fully) balances the frequency of the classes. The method is added in pre-processing.

Tasks

  • add method apply_oversampling in PreProcessing
  • store results of apply_oversampling in data store
  • make all hardcoded parameters (e.g. bins or rates_cap) more flexible
    • add parameter to experiment setup (init, run)
    • load information from data store within apply_oversampling by using data_store.get_default(...)
    • defaults could be either set in the experiment setup (by using the defaults file) or just in the get_default call

The following steps are not specified currently: DataHandler should be able to use the oversampling information

Edited Jun 22, 2021 by Vincent Gramlich
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