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Comprehensive Guide on sklearn's LabelEncoder
schedule Mar 5, 2023
Last updated local_offer
Tags Machine Learning●Python
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The LabelEncoder module in Python's sklearn is used to encode the target labels into categorical integers (e.g. 0, 1, 2, ...).
Encoding numerical target labels
Suppose our target labels are as follows:
raw_y = [6,9,2,5,6]
Our objective is to apply the following mapping:
2: 05: 16: 29: 3
Here, the value 2 gets mapped to 0, the value 5 gets mapped to 1, and so on.
We can make use of LabelEncoder like so:
from sklearn.preprocessing import LabelEncoder
raw_y = [6,9,2,5,6]encoder = LabelEncoder()y = encoder.fit_transform(raw_y) # returns a NumPy arrayy
array([2, 3, 0, 1, 2])
Here, y is the encoded values.
We can access the classes, that is, the unique values in our target like so:
encoder.classes_
array([2, 5, 6, 9])
We can also get the original raw_y using the inverse_transform(~) function:
encoder.inverse_transform(y)
array([6, 9, 2, 5, 6])
Encoding categorical string target labels
The LabelEncoder also works when the target label is categorical:
from sklearn.preprocessing import LabelEncoder
raw_y = ["A","B","A","C"]encoder = LabelEncoder()y = encoder.fit_transform(raw_y)y
array([0, 1, 0, 2])
We can see all our classes like so:
encoder.classes_
array(['A', 'B', 'C'], dtype='<U1')
We can retrieve the original raw_y like so:
encoder.inverse_transform(y)
array(['A', 'B', 'A', 'C'], dtype='<U1')
Published by Isshin Inada
Edited by 0 others
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