sklearn.datasets.make_friedman2

sklearn.datasets.make_friedman2(n_samples=100, noise=0.0, random_state=None) [source]

Generate the “Friedman #2” 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) = (X[:, 0] ** 2 + (X[:, 1] * X[:, 2]  - 1 / (X[:, 1] * X[:, 3])) ** 2) ** 0.5 + 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 (default) Determines random number generation for dataset noise. Pass an int for reproducible output across multiple function calls. See Glossary.
Returns:	X : array of shape [n_samples, 4] The input samples. y : array of shape [n_samples] The output values.

References

[1]	J. Friedman, “Multivariate adaptive regression splines”, The Annals of Statistics 19 (1), pages 1-67, 1991.

[2]	L. Breiman, “Bagging predictors”, Machine Learning 24, pages 123-140, 1996.