# make_regression

### cuml.datasets.make_regression(n_samples=100, n_features=2, n_informative=2, n_targets=1, bias=0.0, effective_rank=None, tail_strength=0.5, noise=0.0, shuffle=True, coef=False, random_state=None, dtype='float32')[[source]](https://github.com/NVIDIA/cuml/blob/93418baf5/python/cuml/cuml/datasets/regression.pyx#L51)

Generate a random regression problem.

See [https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_regression.html](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_regression.html)

* **Parameters:**
  **n_samples**
  : The number of samples.

  **n_features**
  : The number of features.

  **n_informative**
  : The number of informative features, i.e., the number of features used
    to build the linear model used to generate the output.

  **n_targets**
  : 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**
  : The bias term in the underlying linear model.

  **effective_rank**
  : 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.
    <br/>
    if None:
    : The input set is well conditioned, centered and gaussian with
      unit variance.

  **tail_strength**
  : The relative importance of the fat noisy tail of the singular values
    profile if `effective_rank` is not None.

  **noise**
  : The standard deviation of the gaussian noise applied to the output.

  **shuffle**
  : Shuffle the samples and the features.

  **coef**
  : If True, the coefficients of the underlying linear model are returned.

  **random_state**
  : Seed for the random number generator for dataset creation.

  **dtype: string or numpy dtype (default: ‘float32’)**
  : The output dtype. Only ‘float32’ or ‘float64’ supported.
* **Returns:**
  **X**
  : The input samples.

  **y**
  : The output values.

  **coef**
  : The coefficient of the underlying linear model. It is returned only if
    coef is True.

### Examples

```python
>>> from cuml.datasets.regression import make_regression
>>> from cuml.linear_model import LinearRegression

>>> # Create regression problem
>>> data, values = make_regression(n_samples=200, n_features=12,
...                                n_informative=7, bias=-4.2,
...                                noise=0.3, random_state=10)

>>> # Perform a linear regression on this problem
>>> lr = LinearRegression()
>>> reg = lr.fit(data, values)
>>> print(reg.coef_)
[-2.6980877e-02  7.7027252e+01  1.1498465e+01  8.5468025e+00
5.8548538e+01  6.0772545e+01  3.6876743e+01  4.0023815e+01
4.3908358e-03 -2.0275116e-02  3.5066366e-02 -3.4512520e-02]
```

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