sklearn.feature_selection.SelectFpr¶
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class
sklearn.feature_selection.SelectFpr(score_func=<function f_classif>, alpha=0.05)[source]¶ Filter: Select the pvalues below alpha based on a FPR test.
FPR test stands for False Positive Rate test. It controls the total amount of false detections.
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).
alpha : float, optional
The highest p-value for features to be kept.
Attributes: scores_ : array-like, shape=(n_features,)
Scores of features.
pvalues_ : array-like, shape=(n_features,)
p-values of feature scores.
See also
f_classif- ANOVA F-value between labe/feature for classification tasks.
chi2- Chi-squared stats of non-negative features for classification tasks.
f_regression- F-value 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.
SelectFdr- Select features based on an estimated false discovery rate.
SelectFwe- Select features based on family-wise error rate.
GenericUnivariateSelect- Univariate feature selector with configurable mode.
Methods
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. -
fit(X, y)[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).
Returns: self : object
Returns self.
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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.
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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.
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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.
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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.
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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 :