sklearn.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.
Read more in the User Guide.
Parameters: |
X : {array-like, sparse matrix} The data to center and scale. axis : int (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_mean : boolean, True by default If True, center the data before scaling. with_std : boolean, True by default If True, scale the data to unit variance (or equivalently, unit standard deviation). copy : boolean, optional, default True set to False to perform inplace row normalization and avoid a copy (if the input is already a numpy array or a scipy.sparse CSC matrix and if axis is 1). |
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See also
StandardScaler
sklearn.pipeline.Pipeline
).This implementation will refuse to center scipy.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 CSC matrix) or to call X.toarray()
if he/she expects the materialized dense array to fit in memory.
To avoid memory copy the caller should pass a CSC matrix.
sklearn.preprocessing.scale
© 2007–2016 The scikit-learn developers
Licensed under the 3-clause BSD License.
http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.scale.html