sklearn.datasets
.make_friedman3¶
-
sklearn.datasets.
make_friedman3
(n_samples=100, noise=0.0, random_state=None)[source]¶ Generate the “Friedman #3” 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) = arctan((X[:, 1] * X[:, 2] - 1 / (X[:, 1] * X[:, 3])) / X[:, 0]) + noise * N(0, 1).
Read more in the User Guide.
- 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 (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, 4]
The input samples.
y : array of shape [n_samples]
The output values.
References