ProxyClassifier which uses some mapper prior training/testing.
MaskMapper can be used just a subset of features to train/classify. Having such classifier we can easily create a set of classifiers for BoostedClassifier, where each classifier operates on some set of features, e.g. set of best spheres from SearchLight, set of ROIs selected elsewhere. It would be different from simply applying whole mask over the dataset, since here initial decision is made by each classifier and then later on they vote for the final decision across the set of classifiers.
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(*args_, **kwargs_) | |
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() | |
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 the instance
Parameters: | clf : Classifier
mapper :
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(*args_, **kwargs_) | |
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() | |
train(ds) | The default implementation calls _pretrain(), _train(), and finally _posttrain(). |
untrain() | Reverts changes in the state of this node caused by previous training |
Used mapper