mvpa2.datasets.sources.skl_data.skl_sparse_uncorrelated(n_samples=100, n_features=10, random_state=None)

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.


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.


X : array of shape [n_samples, n_features]

The input samples.

y : array of shape [n_samples]

The output values.


This function has been auto-generated by wrapping make_sparse_uncorrelated() from the sklearn package. The documentation of this function has been kept verbatim. Consequently, the actual return value is not as described in the documentation, but the data is returned as a PyMVPA dataset.


[R33]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.