Cross-validate a learner’s transfer on datasets.
A generator is used to resample a dataset into multiple instances (e.g. sets of dataset partitions for leave-one-out folding). For each dataset instance a transfer measure is computed by splitting the dataset into two parts (defined by the dataset generators output space) and train a custom learner on the first part and run it on the next. An arbitray error function can by used to determine the learner’s error when prediction the dataset part that has been unseen during training.
Notes
Available conditional attributes:
(Conditional attributes enabled by default suffixed with +)
Methods
generate(ds) | Yield processing results. |
get_postproc() | Returns the post-processing node or None. |
get_space() | Query the processing space name of this node. |
reset() | |
set_postproc(node) | Assigns a post-processing node |
set_space(name) | Set the processing space name of this node. |
train(ds) | The default implementation calls _pretrain(), _train(), and finally _posttrain(). |
untrain() | Reverts changes in the state of this node caused by previous training |
Parameters : | learner : Learner
generator : Node
errorfx : Node or callable
splitter : Splitter or None
enable_ca : None or list of str
disable_ca : None or list of str
node : Node
callback : functor
concat_as : {‘samples’, ‘features’}
null_dist : instance of distribution estimator
auto_train : bool
force_train : bool
space: str, optional :
postproc : Node instance, optional
descr : str
|
---|
Methods
generate(ds) | Yield processing results. |
get_postproc() | Returns the post-processing node or None. |
get_space() | Query the processing space name of this node. |
reset() | |
set_postproc(node) | Assigns a post-processing node |
set_space(name) | Set the processing space name of this node. |
train(ds) | The default implementation calls _pretrain(), _train(), and finally _posttrain(). |
untrain() | Reverts changes in the state of this node caused by previous training |