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sklearn.datasets.make_friedman1(n_samples=100, n_features=10, noise=0.0, random_state=None)[source] -
Generate the ?Friedman #1? regression problem
This dataset is described in Friedman [1] and Breiman [2].
Inputs
Xare independent features uniformly distributed on the interval [0, 1]. The outputyis created according to the formula:y(X) = 10 * sin(pi * X[:, 0] * X[:, 1]) + 20 * (X[:, 2] - 0.5) ** 2 + 10 * X[:, 3] + 5 * X[:, 4] + noise * N(0, 1).
Out of the
n_featuresfeatures, only 5 are actually used to computey. The remaining features are independent ofy.The number of features has to be >= 5.
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. Should be at least 5.
noise : float, optional (default=0.0)
The standard deviation of the gaussian noise applied to the output.
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
[R137] J. Friedman, ?Multivariate adaptive regression splines?, The Annals of Statistics 19 (1), pages 1-67, 1991. [R138] L. Breiman, ?Bagging predictors?, Machine Learning 24, pages 123-140, 1996.
sklearn.datasets.make_friedman1()
2017-01-15 04:25:54
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