family :
Response type of your targets (either ‘gaussian’ for regression or
‘multinomial’ for classification). (Default: ‘gaussian’)
alpha :
The elastic net mixing parameter. Larger values will give rise to
less L2 regularization, with alpha=1.0 as a true LASSO penalty.
(Default: 1.0)
nlambda :
Maximum number of lambdas to calculate before stopping if not
converged. (Default: 100)
standardize :
Whether to standardize the variables prior to fitting. (Default:
True)
thresh :
Convergence threshold for coordinate descent. (Default: 0.0001)
pmax :
Limit the maximum number of variables ever to be nonzero. (Default:
None)
maxit :
Maximum number of outer-loop iterations for ‘multinomial’ families.
(Default: 100)
model_type :
‘covariance’ saves all inner-products ever computed and can be much
faster than ‘naive’. The latter can be more efficient for
nfeatures>>nsamples situations. (Default: ‘covariance’)
enable_ca : None or list of str
Names of the conditional attributes which should be enabled in addition
to the default ones
disable_ca : None or list of str
Names of the conditional attributes which should be disabled
auto_train : bool
Flag whether the learner will automatically train itself on the input
dataset when called untrained.
force_train : bool
Flag whether the learner will enforce training on the input dataset
upon every call.
space: str, optional :
Name of the ‘processing space’. The actual meaning of this argument
heavily depends on the sub-class implementation. In general, this is
a trigger that tells the node to compute and store information about
the input data that is “interesting” in the context of the
corresponding processing in the output dataset.
postproc : Node instance, optional
Node to perform post-processing of results. This node is applied
in __call__() to perform a final processing step on the to be
result dataset. If None, nothing is done.
descr : str
Description of the instance
|