Class BinarizerScikitsLearnNode

Binarize data (set feature values to 0 or 1) according to a threshold

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

Values greater than the threshold map to 1, while values less than
or equal to the threshold map to 0. With the default threshold of 0,
only positive values map to 1.

Binarization is a common operation on text count data where the
analyst can decide to only consider the presence or absence of a
feature rather than a quantified number of occurrences for instance.

It can also be used as a pre-processing step for estimators that
consider boolean random variables (e.g. modelled using the Bernoulli
distribution in a Bayesian setting).

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

**Parameters**

threshold : float, optional (0.0 by default)
    Feature values below or equal to this are replaced by 0, above it by 1.
    Threshold may not be less than 0 for operations on sparse matrices.

copy : boolean, optional, default True
    set to False to perform inplace binarization and avoid a copy (if
    the input is already a numpy array or a scipy.sparse CSR matrix).

**Notes**

If the input is a sparse matrix, only the non-zero values are subject
to update by the Binarizer class.

This estimator is stateless (besides constructor parameters), the
fit method does nothing but is useful when used in a pipeline.

Overrides: object.__init__

Binarize each element of X

This node has been automatically generated by wrapping the sklearn.preprocessing.data.Binarizer 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 to binarize, element by element. scipy.sparse matrices should be in CSR format to avoid an un-necessary copy.

Overrides: Node.execute

Overrides: Node.is_invertible

(inherited documentation)

Overrides: Node.is_trainable

Do nothing and return the estimator unchanged

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

This method is just there to implement the usual API and hence work in pipelines.

Overrides: Node.stop_training