make_friedman1#

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.

Parameters:

n_samplesint, default=100: The number of samples.
n_featuresint, default=10: The number of features. Should be at least 5.
noisefloat, default=0.0: The standard deviation of the gaussian noise applied to the output.
random_stateint, RandomState instance or None, default=None: Determines random number generation for dataset noise. Pass an int for reproducible output across multiple function calls. See Glossary.

Returns:

Xndarray of shape (n_samples, n_features): The input samples.
yndarray 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.

Examples

>>> from sklearn.datasets import make_friedman1
>>> X, y = make_friedman1(random_state=42)
>>> X.shape
(100, 10)
>>> y.shape
(100,)
>>> list(y[:3])
[np.float64(16.8...), np.float64(5.8...), np.float64(9.4...)]