# scale

### cuml.preprocessing.scale(X, , axis=0, with_mean=True, with_std=True, copy=True)[[source]](https://github.com/NVIDIA/cuml/blob/93418baf5/python/cuml/cuml/_thirdparty/sklearn/preprocessing/_data.py#L96)

Standardize a dataset along any axis

Center to the mean and component wise scale to unit variance.

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

  **axis**
  : axis used to compute the means and standard deviations along. If 0,
    independently standardize each feature, otherwise (if 1) standardize
    each sample.

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

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

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

#### SEE ALSO
[`StandardScaler`](../cuml.preprocessing.StandardScaler/index.html.md#cuml.preprocessing.StandardScaler)
: Performs scaling to unit variance 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_mean=False` (in that case, only variance scaling will be
performed on the features of the sparse matrix) or to densify the matrix
if he/she expects the materialized dense array to fit in memory.

For optimal processing the caller should pass a CSC matrix.

NaNs are treated as missing values: disregarded to compute the statistics,
and maintained during the data transformation.

We use a biased estimator for the standard deviation, equivalent to
`numpy.std(x, ddof=0)`. Note that the choice of `ddof` is unlikely to
affect model performance.

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