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 vote292 views2 answers
Isinstance in python
Python
-1 vote293 views1 answer
Pynput keyboard backspace
Python
0 vote268 views1 answer
Imagedatagenerator split train test
Python
-2 votes262 views4 answers
Django datepicker
Python
0 vote314 views2 answers
How to check sklearn version in cmd
Python
0 vote359 views1 answer
If the value is not in dict return default
Python
9 votes281 views3 answers
Get text between two strings python
Python
1 vote275 views1 answer
How to Add a overall Title to Seaborn Plots
Python
0 vote257 views1 answer
Check if key exists in sesson python flask
Python
3 votes203 views1 answer
Randonm forest python
Python