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 : array-like 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 preprocessing sklearn.pipeline.Pipeline).

Notes

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.