sklearn.preprocessing.scale(X, axis=0, with_mean=True, with_std=True, copy=True)
Standardize a dataset along any axis
Center to the mean and component wise scale to unit variance.
Read more in the User Guide.
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.
NaNs are treated as missing values: disregarded to compute the statistics, and maintained during the data transformation.
For a comparison of the different scalers, transformers, and normalizers, see examples/preprocessing/plot_all_scaling.py.
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Licensed under the 3-clause BSD License.