mvpa.clfs.ridge.RidgeReg¶
- class mvpa.clfs.ridge.RidgeReg(lm=None, **kwargs)¶
Ridge regression Classifier.
This ridge regression adds an intercept term so your labels do not have to be zero-centered.
Notes
Available conditional attributes:
- calling_time+: Time (in seconds) it took to call the node
- estimates+: Internal classifier estimates the most recent predictions are based on
- predicting_time+: Time (in seconds) which took classifier to predict
- predictions+: Most recent set of predictions
- trained_dataset: The dataset it has been trained on
- trained_nsamples+: Number of samples it has been trained on
- trained_targets+: Set of unique targets it has been trained on
- training_stats: Confusion matrix of learning performance
- training_time+: Time (in seconds) it took to train the learner
(Conditional attributes enabled by default suffixed with +)
Initialize a ridge regression analysis.
Parameters : lm : float
the penalty term lambda. (Defaults to .05*nFeatures)
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
