sklearn.datasets.make_classification — scikit-learn 0.20.4 documentation (original) (raw)

Parameters:

n_samples : int, optional (default=100)

The number of samples.

n_features : int, optional (default=20)

The total number of features. These comprise n_informativeinformative features, n_redundant redundant features,n_repeated duplicated features andn_features-n_informative-n_redundant-n_repeated useless features drawn at random.

n_informative : int, optional (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.

n_redundant : int, optional (default=2)

The number of redundant features. These features are generated as random linear combinations of the informative features.

n_repeated : int, optional (default=0)

The number of duplicated features, drawn randomly from the informative and the redundant features.

n_classes : int, optional (default=2)

The number of classes (or labels) of the classification problem.

n_clusters_per_class : int, optional (default=2)

The number of clusters per class.

weights : list of floats or None (default=None)

The proportions of samples assigned to each class. If None, then classes are balanced. Note that if len(weights) == n_classes - 1, then the last class weight is automatically inferred. More than n_samples samples may be returned if the sum ofweights exceeds 1.

flip_y : float, optional (default=0.01)

The fraction of samples whose class are randomly exchanged. Larger values introduce noise in the labels and make the classification task harder.

class_sep : float, optional (default=1.0)

The factor multiplying the hypercube size. Larger values spread out the clusters/classes and make the classification task easier.

hypercube : boolean, optional (default=True)

If True, the clusters are put on the vertices of a hypercube. If False, the clusters are put on the vertices of a random polytope.

shift : float, array of shape [n_features] or None, optional (default=0.0)

Shift features by the specified value. If None, then features are shifted by a random value drawn in [-class_sep, class_sep].

scale : float, array of shape [n_features] or None, optional (default=1.0)

Multiply features by the specified value. If None, then features are scaled by a random value drawn in [1, 100]. Note that scaling happens after shifting.

shuffle : boolean, optional (default=True)

Shuffle the samples and the features.

random_state : int, RandomState instance or None (default)

Determines random number generation for dataset creation. Pass an int for reproducible output across multiple function calls. See Glossary.