Class StandardScalerScikitsLearnNode

Standardize features by removing the mean and scaling to unit variance

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

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).

For instance many elements used in the objective function of
a learning algorithm (such as the RBF kernel of Support Vector
Machines or the L1 and L2 regularizers of linear models) assume that
all features are centered around 0 and have variance in the same
order. If a feature has a variance that is orders of magnitude larger
that others, it might dominate the objective function and make the
estimator unable to learn from other features correctly as expected.

This scaler can also be applied to sparse CSR or CSC matrices by passing
`with_mean=False` to avoid breaking the sparsity structure of the data.

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

**Parameters**

with_mean : 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_std : boolean, True by default
    If True, scale the data to unit variance (or equivalently,
    unit standard deviation).

copy : boolean, optional, 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.

**Attributes**

``scale_`` : ndarray, shape (n_features,)
    Per feature relative scaling of the data.

    .. versionadded:: 0.17
       *scale_* is recommended instead of deprecated *std_*.

``mean_`` : array of floats with shape [n_features]
    The mean value for each feature in the training set.

``var_`` : array of floats with shape [n_features]
    The variance for each feature in the training set. Used to compute
    `scale_`

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

See also

:func:`sklearn.preprocessing.scale` to perform centering and
scaling without using the ``Transformer`` object oriented API

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

Overrides: object.__init__

Perform standardization by centering and scaling

This node has been automatically generated by wrapping the sklearn.preprocessing.data.StandardScaler 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 scale along the features axis.

Overrides: Node.execute

Overrides: Node.is_invertible

(inherited documentation)

Overrides: Node.is_trainable

Compute the mean and std to be used for later scaling.

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

Parameters

X : {array-like, sparse matrix}, shape [n_samples, n_features]

The data used to compute the mean and standard deviation used for later scaling along the features axis.

y: Passthrough for Pipeline compatibility.

Overrides: Node.stop_training