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# `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)

  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:
    mean ndarray of shape (n_features,), default=None

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

    cov float, default=1.0

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

    n_samples int, default=100

      The total number of points equally divided among classes.

    n_features int, default=2

      The number of features for each sample.

    n_classes int, default=3

      The number of classes.

    shuffle bool, default=True

      Shuffle the samples.

    random_state int, 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:
    X ndarray of shape (n_samples, n_features)

      The generated samples.

    y ndarray of shape (n_samples,)

      The integer labels for quantile membership of each sample.

  Notes

  The dataset is from Zhu et al [1].

  References

  [1]

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

## Examples using `sklearn.datasets.make_gaussian_quantiles`

Plot randomly generated classification dataset

Plot randomly generated classification dataset

Multi-class AdaBoosted Decision Trees

Multi-class AdaBoosted Decision Trees

Two-class AdaBoost

Two-class AdaBoost