Class LabelBinarizerScikitsLearnNode

Binarize labels in a one-vs-all fashion

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

Several regression and binary classification algorithms are
available in the scikit. A simple way to extend these algorithms
to the multi-class classification case is to use the so-called
one-vs-all scheme.

At learning time, this simply consists in learning one regressor
or binary classifier per class. In doing so, one needs to convert
multi-class labels to binary labels (belong or does not belong
to the class). LabelBinarizer makes this process easy with the
transform method.

At prediction time, one assigns the class for which the corresponding
model gave the greatest confidence. LabelBinarizer makes this easy
with the inverse_transform method.

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

**Parameters**


neg_label : int (default: 0)
    Value with which negative labels must be encoded.

pos_label : int (default: 1)
    Value with which positive labels must be encoded.

sparse_output : boolean (default: False)
    True if the returned array from transform is desired to be in sparse
    CSR format.

**Attributes**


``classes_`` : array of shape [n_class]
    Holds the label for each class.

``y_type_`` : str,
    Represents the type of the target data as evaluated by
    utils.multiclass.type_of_target. Possible type are 'continuous',
    'continuous-multioutput', 'binary', 'multiclass',
    'mutliclass-multioutput', 'multilabel-indicator', and 'unknown'.

``multilabel_`` : boolean
    True if the transformer was fitted on a multilabel rather than a
    multiclass set of labels. The ``multilabel_`` attribute is deprecated
    and will be removed in 0.18

``sparse_input_`` : boolean,
    True if the input data to transform is given as a sparse matrix, False
    otherwise.

``indicator_matrix_`` : str
    'sparse' when the input data to tansform is a multilable-indicator and
    is sparse, None otherwise. The ``indicator_matrix_`` attribute is
    deprecated as of version 0.16 and will be removed in 0.18


**Examples**

>>> from sklearn import preprocessing
>>> lb = preprocessing.LabelBinarizer()
>>> lb.fit([1, 2, 6, 4, 2])
LabelBinarizer(neg_label=0, pos_label=1, sparse_output=False)
>>> lb.classes_
array([1, 2, 4, 6])
>>> lb.transform([1, 6])
array([[1, 0, 0, 0],
       [0, 0, 0, 1]])

Binary targets transform to a column vector

>>> lb = preprocessing.LabelBinarizer()
>>> lb.fit_transform(['yes', 'no', 'no', 'yes'])
array([[1],
       [0],
       [0],
       [1]])

Passing a 2D matrix for multilabel classification

>>> import numpy as np
>>> lb.fit(np.array([[0, 1, 1], [1, 0, 0]]))
LabelBinarizer(neg_label=0, pos_label=1, sparse_output=False)
>>> lb.classes_
array([0, 1, 2])
>>> lb.transform([0, 1, 2, 1])
array([[1, 0, 0],
       [0, 1, 0],
       [0, 0, 1],
       [0, 1, 0]])

See also

label_binarize : function to perform the transform operation of
    LabelBinarizer with fixed classes.

Overrides: object.__init__

Transform multi-class labels to binary labels

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

The output of transform is sometimes referred to by some authors as the 1-of-K coding scheme.

Parameters

y : numpy array or sparse matrix of shape (n_samples,) or

(n_samples, n_classes) Target values. The 2-d matrix should only contain 0 and 1, represents multilabel classification. Sparse matrix can be CSR, CSC, COO, DOK, or LIL.

Returns

Y : numpy array or CSR matrix of shape [n_samples, n_classes]

Shape will be [n_samples, 1] for binary problems.

Overrides: Node.execute

Overrides: Node.is_invertible

(inherited documentation)

Overrides: Node.is_trainable

Fit label binarizer

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

Parameters

y : numpy array of shape (n_samples,) or (n_samples, n_classes)

Target values. The 2-d matrix should only contain 0 and 1, represents multilabel classification.

Returns

self : returns an instance of self.

Overrides: Node.stop_training