mvpa2.clfs.glmnet.GLMNET_R¶
- class mvpa2.clfs.glmnet.GLMNET_R(**kwargs)¶
GLM-NET Gaussian Regression Classifier.
This is the GLM-NET algorithm from
Friedman, J., Hastie, T. and Tibshirani, R. (2008) Regularization Paths for Generalized Linear Models via Coordinate Descent. http://www-stat.stanford.edu/~hastie/Papers/glmnet.pdf
parameterized to be a regression.
See GLMNET_C for the multinomial classifier version.
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
- raw_results: Computed results before invoking postproc. Stored only if postproc is not None.
- 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 GLM-Net.
See the help in R for further details on the parameters
Parameters : 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
