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.. currentmodule:: sklearn.preprocessing
.. _preprocessing_targets:
==========================================
Transforming the prediction target (``y``)
==========================================
These are transformers that are not intended to be used on features, only on
supervised learning targets. See also :ref:`transformed_target_regressor` if
you want to transform the prediction target for learning, but evaluate the
model in the original (untransformed) space.
Label binarization
==================
LabelBinarizer
--------------
:class:`LabelBinarizer` is a utility class to help create a :term:`label
indicator matrix` from a list of :term:`multiclass` labels::
>>> from sklearn import preprocessing
>>> lb = preprocessing.LabelBinarizer()
>>> lb.fit([1, 2, 6, 4, 2])
LabelBinarizer()
>>> lb.classes_
array([1, 2, 4, 6])
>>> lb.transform([1, 6])
array([[1, 0, 0, 0],
[0, 0, 0, 1]])
Using this format can enable multiclass classification in estimators
that support the label indicator matrix format.
.. warning::
LabelBinarizer is not needed if you are using an estimator that
already supports :term:`multiclass` data.
For more information about multiclass classification, refer to
:ref:`multiclass_classification`.
MultiLabelBinarizer
-------------------
In :term:`multilabel` learning, the joint set of binary classification tasks is
expressed with a label binary indicator array: each sample is one row of a 2d
array of shape (n_samples, n_classes) with binary values where the one, i.e. the
non zero elements, corresponds to the subset of labels for that sample. An array
such as ``np.array([[1, 0, 0], [0, 1, 1], [0, 0, 0]])`` represents label 0 in the
first sample, labels 1 and 2 in the second sample, and no labels in the third
sample.
Producing multilabel data as a list of sets of labels may be more intuitive.
The :class:`MultiLabelBinarizer <sklearn.preprocessing.MultiLabelBinarizer>`
transformer can be used to convert between a collection of collections of
labels and the indicator format::
>>> from sklearn.preprocessing import MultiLabelBinarizer
>>> y = [[2, 3, 4], [2], [0, 1, 3], [0, 1, 2, 3, 4], [0, 1, 2]]
>>> MultiLabelBinarizer().fit_transform(y)
array([[0, 0, 1, 1, 1],
[0, 0, 1, 0, 0],
[1, 1, 0, 1, 0],
[1, 1, 1, 1, 1],
[1, 1, 1, 0, 0]])
For more information about multilabel classification, refer to
:ref:`multilabel_classification`.
Label encoding
==============
:class:`LabelEncoder` is a utility class to help normalize labels such that
they contain only values between 0 and n_classes-1. This is sometimes useful
for writing efficient Cython routines. :class:`LabelEncoder` can be used as
follows::
>>> from sklearn import preprocessing
>>> le = preprocessing.LabelEncoder()
>>> le.fit([1, 2, 2, 6])
LabelEncoder()
>>> le.classes_
array([1, 2, 6])
>>> le.transform([1, 1, 2, 6])
array([0, 0, 1, 2])
>>> le.inverse_transform([0, 0, 1, 2])
array([1, 1, 2, 6])
It can also be used to transform non-numerical labels (as long as they are
hashable and comparable) to numerical labels::
>>> le = preprocessing.LabelEncoder()
>>> le.fit(["paris", "paris", "tokyo", "amsterdam"])
LabelEncoder()
>>> list(le.classes_)
['amsterdam', 'paris', 'tokyo']
>>> le.transform(["tokyo", "tokyo", "paris"])
array([2, 2, 1])
>>> list(le.inverse_transform([2, 2, 1]))
['tokyo', 'tokyo', 'paris']
|
