ML | Label Encoding datasets in Python

Michael Zippo 18.07.2021

Label encoding refers to converting labels to a numeric form in order to convert it to a machine-readable form. Machine learning algorithms can then better figure out how these labels should work. This is an important preprocessing step for a structured dataset in supervised learning.

Example:
Suppose we have a column in some dataset.

After applying the label encoding, the Height column is converted to:

where 0 — label for tall, 1 — label for middle and 2 — for short stature.

We are applying the tag encoding to the iris dataset in the destination column, which is the View. Contains three species Iris-setosa, Iris-versicolor, Iris-virginica .

# Library import

import numpy as np

import pandas as pd

# Dataset Import

df = pd. read_csv ( ’../../ data / Iris.csv’ )

df [ ’species’ ]. unique ()

Exit:

 array ([’Iris-setosa’,’ Iris-versicolor’, ’Iris-virginica’], dtype = object)   After applying Label Encoding — 
  
 
        # Import the label encoder  
   from   sklearn   import   preprocessing  
   
  # label_encoder object knows how to understand word labels.  
   label_encoder   =   preprocessing.LabelEncoder ()  
   
  # Encode labels in the views column.  
   df [  ’species’  ]   =   label_encoder.fit_transform (df [  ’species’  ]) 
   
   df [  ’species’  ]. unique ()  
  
   
   Exit:
  array ([0, 1, 2], dtype = int64) 
   Label constraint Encoding  
 An encoding label converts data into machine-readable form, but assigns a unique number (starting at 0) to each data class. This can lead to the formation of a priority problem when training datasets. A high value label is considered to have higher priority than a lower value label. 
   example
  Attribute having output classes  Mexico ,  Paris ,  Dubai . On the Coding label of this column, let  mexico  be replaced with  0 ,  Paris  replaced with  1,  and  Dubai  is replaced by 2. 
 It can be interpreted that  Dubai  has a higher priority when training the model than  Mexico  and  Paris , but in fact there is no such priority relationship between these cities.