Class RobustScalerScikitsLearnNode

Scale features using statistics that are robust to outliers.

This node has been automatically generated by wrapping the ``sklearn.preprocessing.data.RobustScaler`` class
from the ``sklearn`` library.  The wrapped instance can be accessed
through the ``scikits_alg`` attribute.

This Scaler removes the median and scales the data according to
the Interquartile Range (IQR). 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.

.. versionadded:: 0.17

Read more in the :ref:`User Guide <preprocessing_scaler>`.

**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.

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.

    .. versionadded:: 0.17
       *scale_* attribute.

See also

:class:`sklearn.preprocessing.StandardScaler` to perform centering
and scaling using mean and variance.

:class:`sklearn.decomposition.RandomizedPCA` with `whiten=True`
to further remove the linear correlation across features.

**Notes**

See examples/preprocessing/plot_robust_scaling.py for an example.

http://en.wikipedia.org/wiki/Median_(statistics)
http://en.wikipedia.org/wiki/Interquartile_range

Overrides: object.__init__

Center and scale the data

This node has been automatically generated by wrapping the sklearn.preprocessing.data.RobustScaler class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute.

Parameters

X : array-like

The data used to scale along the specified axis.

Overrides: Node.execute

Overrides: Node.is_invertible

(inherited documentation)

Overrides: Node.is_trainable

Compute the median and quantiles to be used for scaling.

This node has been automatically generated by wrapping the sklearn.preprocessing.data.RobustScaler class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute.

Parameters

X : array-like, shape [n_samples, n_features]

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

Overrides: Node.stop_training