sklearn.preprocessing.RobustScaler  
class sklearn.preprocessing.RobustScaler(*, with_centering=True, with_scaling=True, quantile_range=25.0, 75.0, copy=True, unit_variance=False) [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 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_centeringbool, default=True 

If True, center the data before scaling. This will cause transform to 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_scalingbool, default=True 

If True, scale the data to interquartile range.  
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.   
copybool, default=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.  
unit_variancebool, default=False 

If True, scale data so that normally distributed features have a variance of 1. In general, if the difference between the x-values of q_max and q_min for a standard normal distribution is greater than 1, the dataset will be scaled down. If less than 1, the dataset will be scaled up.  New in version 0.24.     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.  
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 https://en.wikipedia.org/wiki/Interquartile_range Examples >>> from sklearn.preprocessing import RobustScaler
>>> X = [[ 1., -2.,  2.],
...      [ -2.,  1.,  3.],
...      [ 4.,  1., -2.]]
>>> transformer = RobustScaler().fit(X)
>>> transformer
RobustScaler()
>>> transformer.transform(X)
array([[ 0. , -2. ,  0. ],
       [-1. ,  0. ,  0.4],
       [ 1. ,  0. , -1.6]])
 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) Center and scale the data.    
fit(X, y=None) [source]
 
Compute the median and quantiles to be used for scaling.  Parameters 
 
X{array-like, sparse matrix} of shape (n_samples, n_features) 

The data used to compute the median and quantiles used for later scaling along the features axis.  
yNone 

Ignored.    Returns 
 
selfobject 

Fitted scaler.     
  
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 
 
Xarray-like of shape (n_samples, n_features) 

Input samples.  
yarray-like of shape (n_samples,) or (n_samples, n_outputs), default=None 

Target values (None for unsupervised transformations).  
**fit_paramsdict 

Additional fit parameters.    Returns 
 
X_newndarray array of shape (n_samples, n_features_new) 

Transformed array.     
  
get_params(deep=True) [source]
 
Get parameters for this estimator.  Parameters 
 
deepbool, default=True 

If True, will return the parameters for this estimator and contained subobjects that are estimators.    Returns 
 
paramsdict 

Parameter names mapped to their values.     
  
inverse_transform(X) [source]
 
Scale back the data to the original representation  Parameters 
 
X{array-like, sparse matrix} of shape (n_samples, n_features) 

The rescaled data to be transformed back.    Returns 
 
X_tr{ndarray, sparse matrix} of shape (n_samples, n_features) 

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

Estimator parameters.    Returns 
 
selfestimator instance 

Estimator instance.     
  
transform(X) [source]
 
Center and scale the data.  Parameters 
 
X{array-like, sparse matrix} of shape (n_samples, n_features) 

The data used to scale along the specified axis.    Returns 
 
X_tr{ndarray, sparse matrix} of shape (n_samples, n_features) 

Transformed array.     
 
 Examples using sklearn.preprocessing.RobustScaler
 
  Compare the effect of different scalers on data with outliers