jasmine.datasets.generate_classification

jasmine.datasets.generate_classification(n_samples: int = 100, n_features: int = 20, n_informative: int = 5, n_redundant: int = 2, n_classes: int = 2, class_sep: float = 1.0, feature_noise: float = 1.0, redundant_noise: float = 0.0, shuffle: bool = True, random_state: int | None = None) → Tuple[Array, Array][source]

Generate a random n-class classification problem with.

This function creates clusters of points normally distributed around vertices of a hypercube, making it suitable for testing classification algorithms.

Parameters:

  • n_samples – The number of samples.

  • n_features – The total number of features.

  • n_informative – The number of informative features.

  • n_redundant – The number of redundant features (linear combinations of informative features).

  • n_classes – The number of classes (or labels).

  • class_sep – Factor multiplying the hypercube size. Larger values spread out the classes and make the problem easier.

  • shuffle – Whether to shuffle the features.

  • random_state – Seed for the random number generator.

Returns:

A tuple (X, y) where X is the feature matrix and y are the integer labels.