sklearn.preprocessing.robust_scale¶
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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
- Xarray-like
The data to center and scale.
- axisint (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_centeringboolean, True by default
If True, center the data before scaling.
- with_scalingboolean, True by default
If True, scale the data to unit variance (or equivalently, unit standard deviation).
- quantile_rangetuple (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.
- copyboolean, 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
RobustScalerPerforms centering and scaling using the
TransformerAPI (e.g. as part of a preprocessingsklearn.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 callX.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.