The method alternates between training a linear SVM and removing the features
with the smallest value of the weight vector.
You can either choose the number of features or let RFE choose the number
of features automatically; this is chosen as the minimal number of features
such that the number of support vectors is within one standard deviation
from the minimum number of support vectors.
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__init__(self,
arg=None,
**settings)
x.__init__(...) initializes x; see x.__class__.__doc__ for signature |
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rank(self,
data,
*options,
**args)
Returns:
a ranking of the features in the dataset by converting the scores
to ranks |
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Inherited from object :
__delattr__ ,
__getattribute__ ,
__hash__ ,
__new__ ,
__reduce__ ,
__reduce_ex__ ,
__setattr__ ,
__str__
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score(self,
data,
**args)
Returns:
a score for each feature in the input dataset |
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select(self,
data,
*options,
**args)
invokes selectFeatures to find predictive features and eliminates
the rest of the features from the input dataset |
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train(self,
data,
*options,
**args)
invokes selectFeatures to find predictive features and eliminates
the rest of the features from the input dataset |
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