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Feature Encoding Techniques – Machine Learning

  • Difficulty Level : Easy
  • Last Updated : 18 Apr, 2022

As we all know that better encoding leads to a better model and most of the algorithms cannot handle the categorical variables unless they are converted into a numerical value.
Categorical features are generally divided into 3 types: 
 

  1. Binary: Either/or 
    Examples: 
    • Yes, No
    • True, False
  2. Ordinal: Specific ordered Groups. 
    Examples: 
    • low, medium, high
    • cold, hot, lava Hot
  3. Nominal: Unordered Groups. 
    Examples 
    • cat, dog, tiger
    • pizza, burger, coke

Dataset: To download the file click on the link. 
encoding dataset
Code: 
 

Python3




# data preprocessing
import pandas as pd 
# for linear calculations
import numpy as np   
# Plotting Graphs
import seaborn as sns
df = pd.read_csv("Encoding Data.csv")
# displaying top 10 results
df.head(10)  

Output: 
 

Dataset

Lets’ examine the columns of the dataset with different types of encoding techniques. 
Code: Mapping binary features present in the dataset. 
 

Python3




# you can always use simple mapping on binary features.
df['bin_1'] = df['bin_1'].apply(lambda x: 1 if x =='T' else (0 if x =='F' else None))
df['bin_2'] = df['bin_2'].apply(lambda x: 1 if x =='Y' else (0 if x =='N' else None))
sns.countplot(df['bin_1'])
sns.countplot(df['bin_2'])

Output:

Bin_1 after applying mapping

 

bin_2 after applying mapping

Label Encoding: Label encoding algorithm is quite simple and it considers an order for encoding, Hence can be used for encoding ordinal data. 
Code: 
 

Python3




# labelEncoder present in scikitlearn library
from sklearn.preprocessing import LabelEncoder 
le = LabelEncoder()
df['ord_2'] = le.fit_transform(df['ord_2'])
sns.set(style ="darkgrid")
sns.countplot(df['ord_2'])

Output:

Plot of ord_2 after label encoding

One-Hot Encoding: To overcome the Disadvantage of Label Encoding as it considers some hierarchy in the columns which can be misleading to nominal features present in the data. we can use One-Hot Encoding strategy. 
One-hot encoding is processed in 2 steps:

  1. Splitting of categories to different columns.
  2. Put ‘0 for others and ‘1’ as an indicator for the appropriate column.

Code: One-Hot encoding with Sklearn library 
 

Python3




from sklearn.preprocessing import OneHotEncoder
enc = OneHotEncoder()
# transforming the column after fitting
enc = enc.fit_transform(df[['nom_0']]).toarray()
# converting arrays to a dataframe
encoded_colm = pd.DataFrame(enc)
# concatenating dataframes
df = pd.concat([df, encoded_colm], axis = 1)
# removing the encoded column.
df = df.drop(['nom_0'], axis = 1)
df.head(10)

Output:

Output

Code: One-Hot encoding with pandas 
 

Python3




df = pd.get_dummies(df, prefix = ['nom_0'], columns = ['nom_0'])
df.head(10)

Output:

output

This method is more preferable since it gives good labels. 
Note: One-hot encoding approach eliminates the order but it causes the number of columns to expand vastly. So for columns with more unique values try using other techniques.
Frequency Encoding: We can also encode considering the frequency distribution. This method can be effective at times for nominal features.
Code: 
 

Python3




# grouping by frequency
fq = df.groupby('nom_0').size()/len(df)  
# mapping values to dataframe
df.loc[:, "{}_freq_encode".format('nom_0')] = df['nom_0'].map(fq) 
# drop original column.
df = df.drop(['nom_0'], axis = 1)
fq.plot.bar(stacked = True
df.head(10)

Output:

Frequency distribution (fq)

 

Output

Ordinal Encoding: We can use Ordinal Encoding provided in Scikit learn class to encode Ordinal features. It ensures that ordinal nature of the variables is sustained. 
Code: Using Scikit learn. 
 

Python3




from sklearn.preprocessing import OrdinalEncoder
ord1 = OrdinalEncoder()
# fitting encoder
ord1.fit([df['ord_2']])
# transforming the column after fitting
df["ord_2"]= ord1.fit_transform(df[["ord_2"]])
df.head(10)

Output:

Output

Code: Manually assigning ranking by using dictionary 
 

Python3




# creating a dictionary
temp_dict ={'Cold':1, 'Warm':2, 'Hot':3}
# mapping values in column from dictionary
df['Ord_2_encod']= df.ord_2.map(temp_dict)
df = df.drop(['ord_2'], axis = 1)
<strong>Output: </strong>


Output

Binary Encoding: 
Initially categories are encoded as Integer and then converted into binary code, then the digits from that binary string are placed into separate columns. 
for eg: for 7 : 1 1 1 
This method is quite preferable when there are more number of categories. Imagine if you have 100 different categories. One hot encoding will create 100 different columns, But binary encoding only need 7 columns. 
Code:
 

Python3




from category_encoders import BinaryEncoder
encoder = BinaryEncoder(cols =['ord_2'])
# transforming the column after fitting
newdata = encoder.fit_transform(df['ord_2'])
# concatenating dataframe
df = pd.concat([df, newdata], axis = 1)
# dropping old column
df = df.drop(['ord_2'], axis = 1)
df.head(10)

Output:

Output

HashEncoding: Hashing is the process of converting of a string of characters into a unique hash value with applying a hash function. This process is quite useful as it can deal with a higher number of categorical data and its low memory usage. 
Article regarding hashing 
Code: 
 

Python3




from sklearn.feature_extraction import FeatureHasher
# n_features contains the number of bits you want in your hash value.
h = FeatureHasher(n_features = 3, input_type ='string')
# transforming the column after fitting
hashed_Feature = h.fit_transform(df['nom_0'])
hashed_Feature = hashed_Feature.toarray()
df = pd.concat([df, pd.DataFrame(hashed_Feature)], axis = 1)
df.head(10)

Output:

Output

You can further drop the converted feature from your Dataframe. 
Mean/Target Encoding: Target encoding is good because it picks up values that can explain the target. It is used by most kagglers in their competitions. The basic idea to replace a categorical value with the mean of the target variable. 
Code: 
 

Python3




# inserting Target column in the dataset since it needs a target
df.insert(5, "Target", [0, 1, 1, 0, 0, 1, 0, 0, 0, 1], True)
# importing TargetEncoder
from category_encoders import TargetEncoder
Targetenc = TargetEncoder()
# transforming the column after fitting
values = Targetenc.fit_transform(X = df.nom_0, y = df.Target)
# concatenating values with dataframe
df = pd.concat([df, values], axis = 1)
df.head(10)

You can further drop the converted feature from your Dataframe. 
Output:

output

 


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