package sklearn
Module Feature_selection.SelectPercentileSource
Select features according to a percentile of the highest scores.
Read more in the :ref:`User Guide <univariate_feature_selection>`.
Parameters ---------- score_func : callable Function taking two arrays X and y, and returning a pair of arrays (scores, pvalues) or a single array with scores. Default is f_classif (see below 'See also'). The default function only works with classification tasks.
.. versionadded:: 0.18
percentile : int, optional, default=10 Percent of features to keep.
Attributes ---------- scores_ : array-like of shape (n_features,) Scores of features.
pvalues_ : array-like of shape (n_features,) p-values of feature scores, None if `score_func` returned only scores.
Examples -------- >>> from sklearn.datasets import load_digits >>> from sklearn.feature_selection import SelectPercentile, chi2 >>> X, y = load_digits(return_X_y=True) >>> X.shape (1797, 64) >>> X_new = SelectPercentile(chi2, percentile=10).fit_transform(X, y) >>> X_new.shape (1797, 7)
Notes ----- Ties between features with equal scores will be broken in an unspecified way.
See also -------- f_classif: ANOVA F-value between label/feature for classification tasks. mutual_info_classif: Mutual information for a discrete target. chi2: Chi-squared stats of non-negative features for classification tasks. f_regression: F-value between label/feature for regression tasks. mutual_info_regression: Mutual information for a continuous target. SelectKBest: Select features based on the k highest scores. SelectFpr: Select features based on a false positive rate test. 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.
Run score function on (X, y) and get the appropriate features.
Parameters ---------- X : array-like of shape (n_samples, n_features) The training input samples.
y : array-like of shape (n_samples,) The target values (class labels in classification, real numbers in regression).
Returns ------- self : object
Source
val fit_transform :
?y:[> `ArrayLike ] Np.Obj.t ->
?fit_params:(string * Py.Object.t) list ->
x:[> `ArrayLike ] Np.Obj.t ->
[> tag ] Obj.t ->
[> `ArrayLike ] Np.Obj.tFit 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 : array-like, sparse matrix, dataframe of shape (n_samples, n_features)
y : ndarray of shape (n_samples,), default=None Target values.
**fit_params : dict Additional fit parameters.
Returns ------- X_new : ndarray array of shape (n_samples, n_features_new) Transformed array.
Get parameters for this estimator.
Parameters ---------- deep : bool, default=True 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 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.
Source
val inverse_transform :
x:[> `ArrayLike ] Np.Obj.t ->
[> tag ] Obj.t ->
[> `ArrayLike ] Np.Obj.tReverse 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 :meth:`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.
Parameters ---------- **params : dict Estimator parameters.
Returns ------- self : object Estimator instance.
Reduce X to the selected features.
Parameters ---------- X : array of shape n_samples, n_features The input samples.
Returns ------- X_r : array of shape n_samples, n_selected_features The input samples with only the selected features.
Attribute scores_: get value or raise Not_found if None.
Attribute scores_: get value as an option.
Attribute pvalues_: get value or raise Not_found if None.
Attribute pvalues_: get value as an option.
Pretty-print the object to a formatter.