sklearn.datasets
.make_regression¶

sklearn.datasets.
make_regression
(n_samples=100, n_features=100, *, n_informative=10, n_targets=1, bias=0.0, effective_rank=None, tail_strength=0.5, noise=0.0, shuffle=True, coef=False, random_state=None)[source]¶ Generate a random regression problem.
The input set can either be well conditioned (by default) or have a low rankfat tail singular profile. See
make_low_rank_matrix
for more details.The output is generated by applying a (potentially biased) random linear regression model with
n_informative
nonzero regressors to the previously generated input and some gaussian centered noise with some adjustable scale.Read more in the User Guide.
 Parameters
 n_samplesint, default=100
The number of samples.
 n_featuresint, default=100
The number of features.
 n_informativeint, default=10
The number of informative features, i.e., the number of features used to build the linear model used to generate the output.
 n_targetsint, default=1
The number of regression targets, i.e., the dimension of the y output vector associated with a sample. By default, the output is a scalar.
 biasfloat, default=0.0
The bias term in the underlying linear model.
 effective_rankint, default=None
 if not None:
The approximate number of singular vectors required to explain most of the input data by linear combinations. Using this kind of singular spectrum in the input allows the generator to reproduce the correlations often observed in practice.
 if None:
The input set is well conditioned, centered and gaussian with unit variance.
 tail_strengthfloat, default=0.5
The relative importance of the fat noisy tail of the singular values profile if
effective_rank
is not None. When a float, it should be between 0 and 1. noisefloat, default=0.0
The standard deviation of the gaussian noise applied to the output.
 shufflebool, default=True
Shuffle the samples and the features.
 coefbool, default=False
If True, the coefficients of the underlying linear model are returned.
 random_stateint or RandomState instance, default=None
Determines random number generation for dataset creation. Pass an int for reproducible output across multiple function calls. See Glossary.
 Returns
 Xarray of shape [n_samples, n_features]
The input samples.
 yarray of shape [n_samples] or [n_samples, n_targets]
The output values.
 coefarray of shape (n_features,) or (n_features, n_targets)
The coefficient of the underlying linear model. It is returned only if coef is True.