sklearn.datasets.make_gaussian_quantiles

sklearn.datasets.make_gaussian_quantiles(*, mean=None, cov=1.0, n_samples=100, n_features=2, n_classes=3, shuffle=True, random_state=None) [source]

Generate isotropic Gaussian and label samples by quantile.

This classification dataset is constructed by taking a multi-dimensional standard normal distribution and defining classes separated by nested concentric multi-dimensional spheres such that roughly equal numbers of samples are in each class (quantiles of the \(\chi^2\) distribution).

Read more in the User Guide.

Parameters

meanndarray of shape (n_features,), default=None: The mean of the multi-dimensional normal distribution. If None then use the origin (0, 0, …).
covfloat, default=1.0: The covariance matrix will be this value times the unit matrix. This dataset only produces symmetric normal distributions.
n_samplesint, default=100: The total number of points equally divided among classes.
n_featuresint, default=2: The number of features for each sample.
n_classesint, default=3: The number of classes
shufflebool, default=True: Shuffle the samples.
random_stateint, RandomState instance or None, default=None: Determines random number generation for dataset creation. Pass an int for reproducible output across multiple function calls. See Glossary.

Returns

Xndarray of shape (n_samples, n_features): The generated samples.
yndarray of shape (n_samples,): The integer labels for quantile membership of each sample.