Compound comparisons via univariate ANOVA.
This measure compute an ANOVA F-score per each feature, for each one-vs-rest comparision for all unique labels in a dataset. Each F-score vector for each comparision is included in the return datasets as a separate samples. Corresponding p-values are avialable in feature attributes named ‘fprob_X’, where X is the name of the actual comparision label. Note that p-values are only available, if SciPy is installed. The comparison labels for each F-vectore are also stored as ‘targets’ sample attribute in the returned dataset.
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 : | space : str
enable_ca : None or list of str
disable_ca : None or list of str
null_dist : instance of distribution estimator
auto_train : bool
force_train : bool
pass_attr : str, list of str|tuple, optional
postproc : Node instance, optional
descr : str
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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 |