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]
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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: |
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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: |
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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.
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Examples using sklearn.datasets.make_gaussian_quantiles