sklearn.datasets.make_friedman1¶
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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 output y is 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_features features, only 5 are actually used to compute y. The remaining features are independent of y.
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 (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, n_features]
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.