8.8.5. sklearn.feature_selection.SelectFwe

class sklearn.feature_selection.SelectFwe(score_func, alpha=0.05)

Filter: Select the p-values corresponding to Family-wise error rate

Parameters :

score_func: callable :

function taking two arrays X and y, and returning 2 arrays: both scores and pvalues

alpha: float, optional :

the highest uncorrected p-value for features to keep

Methods

fit(X, y)	Evaluate the function
fit_transform(X[, y])	Fit to data, then transform it
get_support([indices])	Return a mask, or list, of the features/indices selected.
inverse_transform(X)	Transform a new matrix using the selected features
set_params(**params)	Set the parameters of the estimator.
transform(X)	Transform a new matrix using the selected features

__init__(score_func, alpha=0.05)

fit(X, y)

Evaluate the function

fit_transform(X, y=None, **fit_params)

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.

Notes

This method just calls fit and transform consecutively, i.e., it is not an optimized implementation of fit_transform, unlike other transformers such as PCA.

get_support(indices=False)

Return a mask, or list, of the features/indices selected.

inverse_transform(X)

Transform a new matrix using the selected features

set_params(**params)

Set the parameters of the 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 :

transform(X)

Transform a new matrix using the selected features