sklearn.preprocessing
.RobustScaler¶

class
sklearn.preprocessing.
RobustScaler
(with_centering=True, with_scaling=True, quantile_range=(25.0, 75.0), copy=True)[source]¶ Scale features using statistics that are robust to outliers.
This Scaler removes the median and scales the data according to the quantile range (defaults to IQR: Interquartile Range). The IQR is the range between the 1st quartile (25th quantile) and the 3rd quartile (75th quantile).
Centering and scaling happen independently on each feature (or each sample, depending on the axis argument) by computing the relevant statistics on the samples in the training set. Median and interquartile range are then stored to be used on later data using the transform method.
Standardization of a dataset is a common requirement for many machine learning estimators. Typically this is done by removing the mean and scaling to unit variance. However, outliers can often influence the sample mean / variance in a negative way. In such cases, the median and the interquartile range often give better results.
New in version 0.17.
Read more in the User Guide.
Parameters: with_centering : boolean, True by default
If True, center the data before scaling. This does not work (and will raise an exception) when attempted on sparse matrices, because centering them entails building a dense matrix which in common use cases is likely to be too large to fit in memory.
with_scaling : boolean, True by default
If True, scale the data to interquartile range.
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
If False, try to avoid a copy and do inplace scaling instead. This is not guaranteed to always work inplace; e.g. if the data is not a NumPy array or scipy.sparse CSR matrix, a copy may still be returned.
Attributes: center_ : array of floats
The median value for each feature in the training set.
scale_ : array of floats
The (scaled) interquartile range for each feature in the training set.
New in version 0.17: scale_ attribute.
See also
robust_scale
 Equivalent function without the estimator API.
sklearn.decomposition.PCA
 Further removes the linear correlation across features with ‘whiten=True’.
Notes
For a comparison of the different scalers, transformers, and normalizers, see examples/preprocessing/plot_all_scaling.py.
https://en.wikipedia.org/wiki/Median_(statistics) https://en.wikipedia.org/wiki/Interquartile_range
Methods
fit
(X[, y])Compute the median and quantiles to be used for scaling. fit_transform
(X[, y])Fit to data, then transform it. get_params
([deep])Get parameters for this estimator. inverse_transform
(X)Scale back the data to the original representation set_params
(**params)Set the parameters of this estimator. transform
(X[, y])Center and scale the data 
fit
(X, y=None)[source]¶ Compute the median and quantiles to be used for scaling.
Parameters: X : arraylike, shape [n_samples, n_features]
The data used to compute the median and quantiles used for later scaling along the features axis.

fit_transform
(X, y=None, **fit_params)[source]¶ Fit to data, then transform it.
Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.
Parameters: X : numpy array of shape [n_samples, n_features]
Training set.
y : numpy array of shape [n_samples]
Target values.
Returns: X_new : numpy array of shape [n_samples, n_features_new]
Transformed array.

get_params
(deep=True)[source]¶ Get parameters for this estimator.
Parameters: deep : boolean, optional
If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns: params : mapping of string to any
Parameter names mapped to their values.

inverse_transform
(X)[source]¶ Scale back the data to the original representation
Parameters: X : arraylike
The data used to scale along the specified axis.

set_params
(**params)[source]¶ Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form
<component>__<parameter>
so that it’s possible to update each component of a nested object.Returns: self :