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: |
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Returns: |
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[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. |
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Licensed under the 3-clause BSD License.
http://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_friedman1.html