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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
[R145] 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.
sklearn.datasets.make_sparse_uncorrelated()
2017-01-15 04:25:59
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