.. _`Standard Scaler`:

.. _`org.sysess.sympathy.machinelearning.standard_scaler`:

Standard Scaler
~~~~~~~~~~~~~~~

.. image:: scaler.svg
   :width: 48


Standardize features by removing the mean and scaling to unit variance.

Centering and scaling happen independently on each feature by
computing the relevant statistics on the samples in the training
set. Mean and standard deviation 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: they might
behave badly if the individual feature do not more or less look like
standard normally distributed data (e.g. Gaussian with 0 mean and unit
variance).

:Configuration:


  - *with_mean*

    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_std*

    If True, scale the data to unit variance (or equivalently,
    unit standard deviation).




:Attributes:


  - *scale_*

    Per feature relative scaling of the data.

    .. versionadded:: 0.17
       *scale_*


  - *mean_*

    The mean value for each feature in the training set.


  - *var_*

    The variance for each feature in the training set. Used to compute
    `scale_`


  - *n_samples_seen_*

    The number of samples processed by the estimator. Will be reset on
    new calls to fit, but increments across ``partial_fit`` calls.




:Inputs:


:Outputs:
    **model** : model
        Model

*Ports*:

    **Outputs**:

        :model: model

            Model

*Configuration*:

    **with_mean**
        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_std**
        If True, scale the data to unit variance (or equivalently,
        unit standard deviation).

.. automodule:: node_preprocessing

.. class:: StandardScaler