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.


mean : array of shape [n_features], optional (default=None)

The mean of the multi-dimensional normal distribution. If None then use the origin (0, 0, …).

cov : float, optional (default=1.)

The covariance matrix will be this value times the unit matrix. This dataset only produces symmetric normal distributions.

n_samples : int, optional (default=100)

The total number of points equally divided among classes.

n_features : int, optional (default=2)

The number of features for each sample.

n_classes : int, optional (default=3)

The number of classes

shuffle : boolean, optional (default=True)

Shuffle the samples.

random_state : int, RandomState instance or None, optional (default=None)

If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random.


X : array of shape [n_samples, n_features]

The generated samples.

y : array of shape [n_samples]

The integer labels for quantile membership of each sample.


The dataset is from Zhu et al [1].


  1. Zhu, H. Zou, S. Rosset, T. Hastie, “Multi-class AdaBoost”, 2009.