sklearn.datasets.make_friedman3 — scikit-learn 0.20.4 documentation (original) (raw)
sklearn.datasets. make_friedman3(n_samples=100, noise=0.0, random_state=None)[source]¶
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 (default) Determines random number generation for dataset noise. Pass an int for reproducible output across multiple function calls. See Glossary. |
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| 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. |
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| [2] | L. Breiman, “Bagging predictors”, Machine Learning 24, pages 123-140, 1996. |
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