sklearn.datasets.make_sparse_uncorrelated

sklearn.datasets.make_sparse_uncorrelated(n_samples=100, n_features=10, random_state=None) [source]

Generate a random regression problem with sparse uncorrelated design

This dataset is described in Celeux et al [1]. as:

X ~ N(0, 1)
y(X) = X[:, 0] + 2 * X[:, 1] - 2 * X[:, 2] - 1.5 * X[:, 3]

Only the first 4 features are informative. The remaining features are useless.

Read more in the User Guide.

Parameters:

n_samples : int, optional (default=100) The number of samples. n_features : int, optional (default=10) The number of features. 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.

Returns:

X : array of shape [n_samples, n_features] The input samples. y : array of shape [n_samples] The output values.

References

[R148]

G. Celeux, M. El Anbari, J.-M. Marin, C. P. Robert, “Regularization in regression: comparing Bayesian and frequentist methods in a poorly informative situation”, 2009.