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

11 votes232 views2 answers
Regression python
Python
0 vote124 views1 answer
String to list in python comma
Python
1 vote150 views1 answer
Add list as element in array
Python
3 votes129 views2 answers
Update_or_create django
Python
5 votes138 views2 answers
Group by list python
Python
0 vote147 views1 answer
Update All Python Packages On Windows
Python
1 vote178 views1 answer
Reached 'max' / getOption("max.print")
Python
0 vote139 views1 answer
Csrf token fetch django
Python
3 votes126 views1 answer
Inconsistent use of tabs and spaces in indentationPylance
Python
0 vote119 views1 answer
Python anonymous function
Python