sklearn.datasets.make_gaussian_quantiles¶
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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: - 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 (default)
Determines random number generation for dataset creation. Pass an int for reproducible output across multiple function calls. See Glossary.
Returns: - 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.
Notes
The dataset is from Zhu et al [1].
References
[1] - Zhu, H. Zou, S. Rosset, T. Hastie, “Multi-class AdaBoost”, 2009.
