How to use the confounds.base.DummyDeconfounding function in confounds
To help you get started, we’ve selected a few confounds examples, based on popular ways it is used in public projects.
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raamana / neuropredict / neuropredict / algorithms.py View on Github 
# param_list_values = [('param_1', range_param1),
# ('criterion_2', criteria),
# ]
# elif name in ('residualize_gpr', 'residualize_gaussianprocessregression'):
# from confounds.base import Residualize
# xfm = Residualize(model='GPR')
# param_list_values = [('param_1', range_param1),
# ('criterion_2', criteria),
# ]
elif xfm_name in ('augment', 'pad'):
from confounds.base import Augment
xfm = Augment()
param_list_values = []
elif xfm_name in ('dummy', 'passthrough'):
from confounds.base import DummyDeconfounding
xfm = DummyDeconfounding()
param_list_values = []
else:
raise ValueError('Unrecognized model name! '
'Choose one of Residualize, Augment or Dummy.')
param_grid = make_parameter_grid(xfm_name, param_list_values)
return xfm, xfm_name, param_gridconfounds
Conquering confounds and covariates in machine learning
