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.
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
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).
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.