8.9.3. sklearn.feature_selection.SelectFpr¶
- class sklearn.feature_selection.SelectFpr(score_func=<function f_classif at 0x38597d0>, alpha=0.05)¶
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.
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. Methods
fit(X, y) Evaluate the score function on samples X with outputs y. fit_transform(X[, y]) Fit to data, then transform it get_params([deep]) Get parameters for the estimator 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=<function f_classif at 0x38597d0>, alpha=0.05)¶
- fit(X, y)¶
Evaluate the score function on samples X with outputs y.
Records and selects features according to the p-values output by the score 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.
- get_params(deep=True)¶
Get parameters for the estimator
Parameters : deep: boolean, optional :
If True, will return the parameters for this estimator and contained subobjects that are estimators.
- 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