Warning
DEPRECATED
class sklearn.linear_model.RandomizedLogisticRegression(*args, **kwargs)
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Randomized Logistic Regression
Randomized Logistic Regression works by subsampling the training data and fitting a L1penalized LogisticRegression model where the penalty of a random subset of coefficients has been scaled. By performing this double randomization several times, the method assigns high scores to features that are repeatedly selected across randomizations. This is known as stability selection. In short, features selected more often are considered good features.
Parameters: 


Attributes: 

See also
Stability selection Nicolai Meinshausen, Peter Buhlmann Journal of the Royal Statistical Society: Series B Volume 72, Issue 4, pages 417473, September 2010 DOI: 10.1111/j.14679868.2010.00740.x
>>> from sklearn.linear_model import RandomizedLogisticRegression >>> randomized_logistic = RandomizedLogisticRegression()
fit (X, y)  Fit the model using X, y as training data. 
fit_transform (X[, y])  Fit to data, then transform it. 
get_params ([deep])  Get parameters for this estimator. 
get_support ([indices])  Get a mask, or integer index, of the features selected 
inverse_transform (X)  Reverse the transformation operation 
set_params (**params)  Set the parameters of this estimator. 
transform (X)  Reduce X to the selected features. 
__init__(*args, **kwargs)
[source]
DEPRECATED: The class RandomizedLogisticRegression is deprecated in 0.19 and will be removed in 0.21.
fit(X, y)
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Fit the model using X, y as training data.
Parameters: 


Returns: 

fit_transform(X, y=None, **fit_params)
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Fit to data, then transform it.
Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.
Parameters: 


Returns: 

get_params(deep=True)
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Get parameters for this estimator.
Parameters: 


Returns: 

get_support(indices=False)
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Get a mask, or integer index, of the features selected
Parameters: 


Returns: 

inverse_transform(X)
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Reverse the transformation operation
Parameters: 


Returns: 

set_params(**params)
[source]
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form <component>__<parameter>
so that it’s possible to update each component of a nested object.
Returns: 


transform(X)
[source]
Reduce X to the selected features.
Parameters: 


Returns: 

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Licensed under the 3clause BSD License.
http://scikitlearn.org/stable/modules/generated/sklearn.linear_model.RandomizedLogisticRegression.html