SelectKBest

class ibex.sklearn.feature_selection.SelectKBest(score_func=<function f_classif>, k=10)

Bases: sklearn.feature_selection.univariate_selection.SelectKBest, ibex._base.FrameMixin

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

Select features according to the k highest scores.

Read more in the User Guide.

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.
k : int or “all”, optional, default=10
Number of top features to select. The “all” option bypasses selection, for use in a parameter search.
scores_ : array-like, shape=(n_features,)
Scores of features.
pvalues_ : array-like, shape=(n_features,)
p-values of feature scores, None if score_func returned only scores.

Ties between features with equal scores will be broken in an unspecified way.

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. SelectPercentile: Select features based on percentile of the 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.

fit(X, y)

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

Run score function on (X, y) and get the appropriate features.

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).
self : object
Returns self.
fit_transform(X, y=None, **fit_params)

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

X : numpy array of shape [n_samples, n_features]
Training set.
y : numpy array of shape [n_samples]
Target values.
X_new : numpy array of shape [n_samples, n_features_new]
Transformed array.
inverse_transform(X)

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

Reverse the transformation operation

X : array of shape [n_samples, n_selected_features]
The input samples.
X_r : array of shape [n_samples, n_original_features]
X with columns of zeros inserted where features would have been removed by transform.
transform(X)

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

Reduce X to the selected features.

X : array of shape [n_samples, n_features]
The input samples.
X_r : array of shape [n_samples, n_selected_features]
The input samples with only the selected features.