Least angle regression (LARS).
LARS is the model selection algorithm from:
Bradley Efron, Trevor Hastie, Iain Johnstone and Robert Tibshirani, Least Angle Regression Annals of Statistics (with discussion) (2004) 32(2), 407-499. A new method for variable subset selection, with the lasso and ‘epsilon’ forward stagewise methods as special cases.
Similar to SMLR, it performs a feature selection while performing classification, but instead of starting with all features, it starts with none and adds them in, which is similar to boosting.
This learner behaves more like a ridge regression in that it returns prediction values and it treats the training labels as continuous.
In the true nature of the PyMVPA framework, this algorithm is actually implemented in R by Trevor Hastie and wrapped via RPy. To make use of LARS, you must have R and RPy installed as well as the LARS contributed package. You can install the R and RPy with the following command on Debian-based machines:
sudo aptitude install python-rpy python-rpy-doc r-base-dev
You can then install the LARS package by running R as root and calling:
install.packages()
Notes
Available conditional attributes:
(Conditional attributes enabled by default suffixed with +)
Methods
clone() | Create full copy of the classifier. |
generate(ds) | Yield processing results. |
get_postproc() | Returns the post-processing node or None. |
get_sensitivity_analyzer(**kwargs) | Returns a sensitivity analyzer for LARS. |
get_space() | Query the processing space name of this node. |
is_trained([dataset]) | Either classifier was already trained. |
predict(obj, data, *args, **kwargs) | |
repredict(obj, data, *args, **kwargs) | |
reset() | |
retrain(dataset, **kwargs) | Helper to avoid check if data was changed actually changed |
set_postproc(node) | Assigns a post-processing node |
set_space(name) | Set the processing space name of this node. |
summary() | Providing summary over the classifier |
train(ds) | The default implementation calls _pretrain(), _train(), and finally _posttrain(). |
untrain() | Reverts changes in the state of this node caused by previous training |
Initialize LARS.
See the help in R for further details on the following parameters:
Parameters: | model_type : string
trace : boolean
normalize : boolean
intercept : boolean
max_steps : None or int
use_Gram : boolean
enable_ca : None or list of str
disable_ca : None or list of str
auto_train : bool
force_train : bool
space : str, optional
pass_attr : str, list of str|tuple, optional
postproc : Node instance, optional
descr : str
|
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Methods
clone() | Create full copy of the classifier. |
generate(ds) | Yield processing results. |
get_postproc() | Returns the post-processing node or None. |
get_sensitivity_analyzer(**kwargs) | Returns a sensitivity analyzer for LARS. |
get_space() | Query the processing space name of this node. |
is_trained([dataset]) | Either classifier was already trained. |
predict(obj, data, *args, **kwargs) | |
repredict(obj, data, *args, **kwargs) | |
reset() | |
retrain(dataset, **kwargs) | Helper to avoid check if data was changed actually changed |
set_postproc(node) | Assigns a post-processing node |
set_space(name) | Set the processing space name of this node. |
summary() | Providing summary over the classifier |
train(ds) | The default implementation calls _pretrain(), _train(), and finally _posttrain(). |
untrain() | Reverts changes in the state of this node caused by previous training |
Returns a sensitivity analyzer for LARS.