sklearn.feature_selection
.SelectFdr¶

class
sklearn.feature_selection.
SelectFdr
(score_func=<function f_classif at 0x5441de8>, 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.Parameters: score_func : callable
Function taking two arrays X and y, and returning a pair of arrays (scores, pvalues).
alpha : float, optional
The highest uncorrected pvalue for features to keep.
Attributes: scores_ : arraylike, shape=(n_features,)
Scores of features.
pvalues_ : arraylike, shape=(n_features,)
pvalues of feature scores.
See also
f_classif
 ANOVA Fvalue between labe/feature for classification tasks.
chi2
 Chisquared stats of nonnegative features for classification tasks.
f_regression
 Fvalue between label/feature for regression tasks.
SelectPercentile
 Select features based on percentile of the highest scores.
SelectKBest
 Select features based on the k highest scores.
SelectFpr
 Select features based on a false positive rate test.
SelectFwe
 Select features based on familywise error rate.
GenericUnivariateSelect
 Univariate feature selector with configurable mode.
References
http://en.wikipedia.org/wiki/False_discovery_rate
Methods

fit
(X, y)[source]¶ Run score function on (X, y) and get the appropriate features.
Parameters: X : arraylike, shape = [n_samples, n_features]
The training input samples.
y : arraylike, shape = [n_samples]
The target values (class labels in classification, real numbers in regression).
Returns: self : object
Returns self.

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: X : numpy array of shape [n_samples, n_features]
Training set.
y : numpy array of shape [n_samples]
Target values.
Returns: X_new : numpy array of shape [n_samples, n_features_new]
Transformed array.

get_params
(deep=True)[source]¶ Get parameters for this estimator.
Parameters: deep: boolean, optional :
If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns: params : mapping of string to any
Parameter names mapped to their values.

get_support
(indices=False)[source]¶ Get a mask, or integer index, of the features selected
Parameters: indices : boolean (default False)
If True, the return value will be an array of integers, rather than a boolean mask.
Returns: support : array
An index that selects the retained features from a feature vector. If indices is False, this is a boolean array of shape [# input features], in which an element is True iff its corresponding feature is selected for retention. If indices is True, this is an integer array of shape [# output features] whose values are indices into the input feature vector.

inverse_transform
(X)[source]¶ Reverse the transformation operation
Parameters: X : array of shape [n_samples, n_selected_features]
The input samples.
Returns: X_r : array of shape [n_samples, n_original_features]
X with columns of zeros inserted where features would have been removed by transform.

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 former have parameters of the form
<component>__<parameter>
so that it’s possible to update each component of a nested object.Returns: self :