sklearn.feature_selection.SelectFdr — scikit-learn 0.20.4 documentation (original) (raw)
class sklearn.feature_selection. SelectFdr(score_func=<function f_classif>, alpha=0.05)[source]¶
Filter: Select the p-values for an estimated false discovery rate
This uses the Benjamini-Hochberg procedure. alpha is an upper bound on the expected false discovery rate.
Read more in the User Guide.
| Parameters: | score_func : callable Function taking two arrays X and y, and returning a pair of arrays (scores, pvalues). Default is f_classif (see below “See also”). The default function only works with classification tasks. alpha : float, optional The highest uncorrected p-value for features to keep. |
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| Attributes: | scores_ : array-like, shape=(n_features,) Scores of features. pvalues_ : array-like, shape=(n_features,) p-values of feature scores. |
See also
ANOVA F-value between label/feature for classification tasks.
Mutual information for a discrete target.
Chi-squared stats of non-negative features for classification tasks.
F-value between label/feature for regression tasks.
Mutual information for a contnuous target.
Select features based on percentile of the highest scores.
Select features based on the k highest scores.
Select features based on a false positive rate test.
Select features based on family-wise error rate.
Univariate feature selector with configurable mode.
References
https://en.wikipedia.org/wiki/False_discovery_rate
Examples
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)
Methods
| fit(X, y) | Run score function on (X, y) and get the appropriate features. |
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| 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]¶
Run score function on (X, y) and get the appropriate features.
| Parameters: | X : array-like, shape = [n_samples, n_features] The training input samples. y : array-like, shape = [n_samples] The target values (class labels in classification, real numbers in regression). |
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| Returns: | self : object |
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. |
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| 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. |
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| 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. |
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| 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. |
Reverse the transformation operation
| Parameters: | X : array of shape [n_samples, n_selected_features] The input samples. |
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| 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 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: | self |
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Reduce X to the selected features.
| Parameters: | X : array of shape [n_samples, n_features] The input samples. |
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| Returns: | X_r : array of shape [n_samples, n_selected_features] The input samples with only the selected features. |