General linear model (GLM).
Regressors can be defined in a design matrix and a linear fit of the data is computed univariately (i.e. indepently for each feature). This measure can report ‘raw’ parameter estimates (i.e. beta weights) of the linear model, as well as standardized parameters (z-stat) using an ordinary least squares (aka fixed-effects) approach to estimate the parameter estimate.
The measure is reported in a (nfeatures x nregressors)-shaped array.
Notes
Available conditional attributes:
(Conditional attributes enabled by default suffixed with +)
Parameters : | design : array (nsamples x nregressors)
voi : {‘pe’, ‘zstat’}
enable_ca : None or list of str
disable_ca : None or list of str
null_dist : instance of distribution estimator
auto_train : bool
force_train : bool
space: str, optional :
postproc : Node instance, optional
descr : str
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Indicate that this measure is always trained.