sklearn.preprocessing
.robust_scale¶

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:  X : arraylike
The data to center and scale.
 axis : int (0 by default)
axis used to compute the medians and IQR along. If 0, independently scale each feature, otherwise (if 1) scale each sample.
 with_centering : boolean, True by default
If True, center the data before scaling.
 with_scaling : boolean, True by default
If True, scale the data to unit variance (or equivalently, unit standard deviation).
 quantile_range : tuple (q_min, q_max), 0.0 < q_min < q_max < 100.0
Default: (25.0, 75.0) = (1st quantile, 3rd quantile) = IQR Quantile range used to calculate
scale_
.New in version 0.18.
 copy : boolean, optional, default is 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 CSR matrix and if axis is 1).
See also
RobustScaler
 Performs centering and scaling using the
Transformer
API (e.g. as part of a preprocessingsklearn.pipeline.Pipeline
).
Notes
This implementation will refuse to center scipy.sparse matrices since it would make them nonsparse 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.