sklearn.preprocessing.robust_scale(X, axis=0, with_centering=True, with_scaling=True, quantile_range=(25.0, 75.0), copy=True)
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.
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.
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Licensed under the 3-clause BSD License.