class sklearn.feature_selection.SelectFdr(score_func=<function f_classif>, alpha=0.05)
[source]
Filter: Select the pvalues for an estimated false discovery rate
This uses the BenjaminiHochberg procedure. alpha
is an upper bound on the expected false discovery rate.
Read more in the User Guide.
Parameters: 


Attributes: 

See also
f_classif
mutual_info_classif
chi2
f_regression
mutual_info_regression
SelectPercentile
SelectKBest
SelectFpr
SelectFwe
GenericUnivariateSelect
https://en.wikipedia.org/wiki/False_discovery_rate
>>> from sklearn.datasets import load_breast_cancer >>> from sklearn.feature_selection import SelectFdr, chi2 >>> X, y = load_breast_cancer(return_X_y=True) >>> X.shape (569, 30) >>> X_new = SelectFdr(chi2, alpha=0.01).fit_transform(X, y) >>> X_new.shape (569, 16)
fit (X, y)  Run score function on (X, y) and get the appropriate features. 
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__(score_func=<function f_classif>, alpha=0.05)
[source]
fit(X, y)
[source]
Run score function on (X, y) and get the appropriate features.
Parameters: 


Returns: 

fit_transform(X, y=None, **fit_params)
[source]
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)
[source]
Get parameters for this estimator.
Parameters: 


Returns: 

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


Returns: 

inverse_transform(X)
[source]
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: 

© 2007–2018 The scikitlearn developers
Licensed under the 3clause BSD License.
http://scikitlearn.org/stable/modules/generated/sklearn.feature_selection.SelectFdr.html