ITMO_FS.filters.univariate.gini_index

ITMO_FS.filters.univariate.gini_index(X, y)

Gini index is a measure of statistical dispersion. Note: before counting gini index data is normalized with MinMaxScaler

Parameters:

• X (numpy array, shape (n_samples, n_features)) – The input samples.
• y (numpy array, shape (n_samples, )) – The classes for the samples.

Returns:

Return type:

Score for each feature as a numpy array, shape (n_features, )

See also

https()

//en.wikipedia.org/wiki/Gini_coefficient

Examples

import sklearn.datasets as datasets from ITMO_FS.filters.univariate import gini_index

X, y = datasets.make_classification(n_samples=200, n_features=7, shuffle=False) scores = gini_index(X, y) print(scores)

>>> import sklearn.datasets as datasets
>>> from ITMO_FS.filters.univariate import gini_index
>>> X, y = datasets.make_classification(n_samples=200, n_features=7, shuffle=False)
>>> scores = gini_index(X, y)
>>> print(scores)