[PYTHON] [Translation] scikit-learn 0.18 User Guide 4.8. Convert the prediction target (y)

Google translated http://scikit-learn.org/0.18/modules/preprocessing_targets.html. [scikit-learn 0.18 User Guide 4. Dataset Conversion](http://qiita.com/nazoking@github/items/267f2371757516f8c168#4-%E3%83%87%E3%83%BC%E3%82%BF From% E3% 82% BB% E3% 83% 83% E3% 83% 88% E5% A4% 89% E6% 8F% 9B)

4.8. Convert the prediction target (y)

4.8.1. Label binarization

LabelBinarizer is used to create a label indicator matrix from a list of multiclass labels. A useful utility class:

>>> 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]])

If you want to use multiple labels per instance, use MultiLabelBinarizer (http://scikit-learn.org/0.18/modules/generated/sklearn.preprocessing.MultiLabelBinarizer.html#sklearn.preprocessing.MultiLabelBinarizer) ..

>>> lb = preprocessing.MultiLabelBinarizer()
>>> lb.fit_transform([(1, 2), (3,)])
array([[1, 1, 0],
       [0, 0, 1]])
>>> lb.classes_
array([1, 2, 3])

4.8.2. Label encoding

LabelEncoder should now contain only values between 0 and n_classes-1 A utility class for normalizing labels. This is sometimes useful for writing efficient Cython routines. 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 convert non-numeric labels to numeric labels (if hashable and comparable).

>>> 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']

[scikit-learn 0.18 User Guide 4. Dataset Conversion](http://qiita.com/nazoking@github/items/267f2371757516f8c168#4-%E3%83%87%E3%83%BC%E3%82%BF From% E3% 82% BB% E3% 83% 83% E3% 83% 88% E5% A4% 89% E6% 8F% 9B)

© 2010 --2016, scikit-learn developers (BSD license).