lm :
The penalty term lambda. Larger values will give rise to more
sparsification. (Default: 0.1)
convergence_tol :
When the weight change for each cycle drops below this value the
regression is considered converged. Smaller values lead to tighter
convergence. (Default: 0.001)
resamp_decay :
Decay rate in the probability of resampling a zero weight. 1.0 will
immediately decrease to the min_resamp from 1.0, 0.0 will never
decrease from 1.0. (Default: 0.5)
min_resamp :
Minimum resampling probability for zeroed weights. (Default: 0.001)
maxiter :
Maximum number of iterations before stopping if not converged.
(Default: 10000)
has_bias :
Whether to add a bias term to allow fits to data not through zero.
(Default: True)
fit_all_weights :
Whether to fit weights for all classes or to the number of classes
minus one. Both should give nearly identical results, but if you set
fit_all_weights to True it will take a little longer and yield
weights that are fully analyzable for each class. Also, note that
the convergence rate may be different, but convergence point is the
same. (Default: True)
implementation :
Use C or Python as the implementation of stepwise_regression. C
version brings significant speedup thus is the default one.
(Default: ‘C’)
ties :
Resolve ties which could occur. At the moment only obvious ties
resulting in identical weights per two classes are detected and
resolved randomly by injecting small amount of noise into the
estimates of tied categories. Set to False to avoid this behavior.
(Default: ‘random’)
seed :
Seed to be used to initialize random generator, might be used to
replicate the run. (Default: 873628505)
unsparsify :
*EXPERIMENTAL* Whether to unsparsify the weights via regression.
Note that it likely leads to worse classifier performance, but more
interpretable weights. (Default: False)
std_to_keep :
Standard deviation threshold of weights to keep when unsparsifying.
(Default: 2.0)
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
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