sklearn.preprocessing.robust_scale(X, axis=0, with_centering=True, with_scaling=True, quantile_range=(25.0, 75.0), copy=True)
[source]
Standardize a dataset along any axis
Center to the median and component wise scale according to the interquartile range.
Read more in the User Guide.
Parameters: |
|
---|
See also
RobustScaler
Transformer
API (e.g. as part of a preprocessing 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_centering=False
(in that case, only variance scaling will be performed on the features of the CSR 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 CSR matrix.
For a comparison of the different scalers, transformers, and normalizers, see examples/preprocessing/plot_all_scaling.py.
© 2007–2018 The scikit-learn developers
Licensed under the 3-clause BSD License.
http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.robust_scale.html