Function make_classification

sklearn/datasets/_samples_generator.py:67–387

Generate a random n-class classification problem. This initially creates clusters of points normally distributed (std=1) about vertices of an ``n_informative``-dimensional hypercube with sides of length ``2*class_sep`` and assigns an equal number of clusters to each class. It introd

(
    n_samples=100,
    n_features=20,
    *,
    n_informative=2,
    n_redundant=2,
    n_repeated=0,
    n_classes=2,
    n_clusters_per_class=2,
    weights=None,
    flip_y=0.01,
    class_sep=1.0,
    hypercube=True,
    shift=0.0,
    scale=1.0,
    shuffle=True,
    random_state=None,
    return_X_y=True,
)

    prefer_skip_nested_validation=True,
)
def make_classification(
    n_samples=100,
    n_features=20,
    *,
    n_informative=2,
    n_redundant=2,
    n_repeated=0,
    n_classes=2,
    n_clusters_per_class=2,
    weights=None,
    flip_y=0.01,
    class_sep=1.0,
    hypercube=True,
    shift=0.0,
    scale=1.0,
    shuffle=True,
    random_state=None,
    return_X_y=True,
):
    """Generate a random n-class classification problem.

    This initially creates clusters of points normally distributed (std=1)
    about vertices of an ``n_informative``-dimensional hypercube with sides of
    length ``2*class_sep`` and assigns an equal number of clusters to each
    class. It introduces interdependence between these features and adds
    various types of further noise to the data.

    Without shuffling, ``X`` horizontally stacks features in the following
    order: the primary ``n_informative`` features, followed by ``n_redundant``
    linear combinations of the informative features, followed by ``n_repeated``
    duplicates, drawn randomly with replacement from the informative and
    redundant features. The remaining features are filled with random noise.
    Thus, without shuffling, all useful features are contained in the columns
    ``X[:, :n_informative + n_redundant + n_repeated]``.

    Read more in the :ref:`User Guide <sample_generators>`.

    Parameters
    ----------
    n_samples : int, default=100
        The number of samples.

    n_features : int, default=20
        The total number of features. These comprise ``n_informative``
        informative features, ``n_redundant`` redundant features,
        ``n_repeated`` duplicated features and
        ``n_features-n_informative-n_redundant-n_repeated`` useless features
        drawn at random.

    n_informative : int, default=2
        The number of informative features. Each class is composed of a number
        of gaussian clusters each located around the vertices of a hypercube
        in a subspace of dimension ``n_informative``. For each cluster,
        informative features are drawn independently from  N(0, 1) and then
        randomly linearly combined within each cluster in order to add
        covariance. The clusters are then placed on the vertices of the
        hypercube.