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sklearn.datasets.make_friedman3(n_samples=100, noise=0.0, random_state=None)
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Generate the ?Friedman #3? regression problem
This dataset is described in Friedman [1] and Breiman [2].
Inputs
X
are 4 independent features uniformly distributed on the intervals:0 <= X[:, 0] <= 100, 40 * pi <= X[:, 1] <= 560 * pi, 0 <= X[:, 2] <= 1, 1 <= X[:, 3] <= 11.
The output
y
is created according to the formula:y(X) = arctan((X[:, 1] * X[:, 2] - 1 / (X[:, 1] * X[:, 3])) / X[:, 0]) + noise * N(0, 1).
Read more in the User Guide.
Parameters: n_samples : int, optional (default=100)
The number of samples.
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, 4]
The input samples.
y : array of shape [n_samples]
The output values.
References
[R141] J. Friedman, ?Multivariate adaptive regression splines?, The Annals of Statistics 19 (1), pages 1-67, 1991. [R142] L. Breiman, ?Bagging predictors?, Machine Learning 24, pages 123-140, 1996.
sklearn.datasets.make_friedman3()
2017-01-15 04:25:55
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