scale#

cuml.preprocessing.scale(X, *, axis=0, with_mean=True, with_std=True, copy=True)[source]#

Standardize a dataset along any axis

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

Parameters:

X{array-like, sparse matrix}: The data to center and scale.
axisint (0 by default): axis used to compute the means and standard deviations along. If 0, independently standardize each feature, otherwise (if 1) standardize each sample.
with_meanboolean, True by default: If True, center the data before scaling.
with_stdboolean, True by default: If True, scale the data to unit variance (or equivalently, unit standard deviation).
copyboolean, optional, default True: Whether a forced copy will be triggered. If copy=False, a copy might be triggered by a conversion.

See also

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.