Label binarizer

>>> 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]])

Sklearn labelbinarizer in pipeline

from sklearn.preprocessing import LabelBinarizer

class LabelBinarizerPipelineFriendly(LabelBinarizer):
    def fit(self, X, y=None):
        """this would allow us to fit the model based on the X input."""
        super(LabelBinarizerPipelineFriendly,self).fit(X)
    def transform(self, X, y=None):
        return super(LabelBinarizerPipelineFriendly, self).transform(X)
    def fit_transform(self, X, y=None):
        return super(LabelBinarizerPipelineFriendly, self).fit(X).transform(X)

Source: github.com

Similar codes

0 vote153 views1 answer
Image hashing
Python
0 vote202 views1 answer
Purpose of migration folder in django
Python
33 votes149 views2 answers
Este año spanish meaning
Python
1 vote158 views1 answer
Python seek file beginning after for line in file
Python
0 vote189 views1 answer
Auth proxy python
Python
2 votes150 views2 answers
If else in dictionary comprehension python
Python
0 vote148 views1 answer
Apply dictionary to df clumn
Python
0 vote184 views1 answer
Django order by foreign key count
Python Django
0 vote141 views1 answer
Groovey set variable
Python
4 votes190 views1 answer
Django pandas queryset
Python Django