# maxframe.learn.datasets.make_regression

### maxframe.learn.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)

Generate a random regression problem.

The input set can either be well conditioned (by default) or have a low
rank-fat tail singular profile. See [`make_low_rank_matrix()`](maxframe.learn.datasets.make_low_rank_matrix.md#maxframe.learn.datasets.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_samples** ([*int*](https://docs.python.org/3/library/functions.html#int) *,* *default=100*) – The number of samples.
  * **n_features** ([*int*](https://docs.python.org/3/library/functions.html#int) *,* *default=100*) – The number of features.
  * **n_informative** ([*int*](https://docs.python.org/3/library/functions.html#int) *,* *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_targets** ([*int*](https://docs.python.org/3/library/functions.html#int) *,* *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.
  * **bias** ([*float*](https://docs.python.org/3/library/functions.html#float) *,* *default=0.0*) – The bias term in the underlying linear model.
  * **effective_rank** ([*int*](https://docs.python.org/3/library/functions.html#int) *,* *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_strength** ([*float*](https://docs.python.org/3/library/functions.html#float) *,* *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.
  * **noise** ([*float*](https://docs.python.org/3/library/functions.html#float) *,* *default=0.0*) – The standard deviation of the gaussian noise applied to the output.
  * **shuffle** ([*bool*](https://docs.python.org/3/library/functions.html#bool) *,* *default=True*) – Shuffle the samples and the features.
  * **coef** ([*bool*](https://docs.python.org/3/library/functions.html#bool) *,* *default=False*) – If True, the coefficients of the underlying linear model are returned.
  * **random_state** ([*int*](https://docs.python.org/3/library/functions.html#int) *,* *RandomState instance* *or* *None* *,* *default=None*) – Determines random number generation for dataset creation. Pass an int
    for reproducible output across multiple function calls.
    See Glossary.
* **Returns:**
  * **X** (*tensor of shape (n_samples, n_features)*) – The input samples.
  * **y** (*tensor of shape (n_samples,) or (n_samples, n_targets)*) – The output values.
  * **coef** (*tensor of shape (n_features,) or (n_features, n_targets)*) – The coefficient of the underlying linear model. It is returned only if
    coef is True.
