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 X are 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.


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, 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.


X : array of shape [n_samples, n_features]

The input samples.

y : array of shape [n_samples]

The output values.


[R111]J. Friedman, “Multivariate adaptive regression splines”, The Annals of Statistics 19 (1), pages 1-67, 1991.
[R112]L. Breiman, “Bagging predictors”, Machine Learning 24, pages 123-140, 1996.