sklearn.datasets.make_friedman2¶
- sklearn.datasets.make_friedman2(n_samples=100, noise=0.0, random_state=None)¶
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).
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
[R114] J. Friedman, “Multivariate adaptive regression splines”, The Annals of Statistics 19 (1), pages 1-67, 1991. [R115] L. Breiman, “Bagging predictors”, Machine Learning 24, pages 123-140, 1996.