# robust_scale

### cuml.preprocessing.robust_scale(X, , axis=0, with_centering=True, with_scaling=True, quantile_range=(25.0, 75.0), copy=True)[[source]](https://github.com/NVIDIA/cuml/blob/93418baf5/python/cuml/cuml/_thirdparty/sklearn/preprocessing/_data.py#L1425)

Standardize a dataset along any axis

Center to the median and component wise scale
according to the interquartile range.

* **Parameters:**
  **X**
  : The data to center and scale.

  **axis**
  : axis used to compute the medians and IQR along. If 0,
    independently scale each feature, otherwise (if 1) scale
    each sample.

  **with_centering**
  : If True, center the data before scaling.

  **with_scaling**
  : If True, scale the data to unit variance (or equivalently,
    unit standard deviation).

  **quantile_range**
  : Default: (25.0, 75.0) = (1st quantile, 3rd quantile) = IQR
    Quantile range used to calculate `scale_`.

  **copy**
  : Whether a forced copy will be triggered. If copy=False, a copy might
    be triggered by a conversion.

#### SEE ALSO
[`RobustScaler`](../cuml.preprocessing.RobustScaler/index.html.md#cuml.preprocessing.RobustScaler)
: Performs centering and scaling using the `Transformer` API

### Notes

This implementation will refuse to center sparse matrices
since it would make them non-sparse and would potentially crash the
program with memory exhaustion problems.

Instead the caller is expected to either set explicitly
`with_centering=False` (in that case, only variance scaling will be
performed on the features of the CSR matrix) or to densify the matrix
if he/she expects the materialized dense array to fit in memory.

To avoid memory copy the caller should pass a CSR matrix.

<!-- !! processed by numpydoc !! -->