Feature Encoding Techniques - Machine Learning

As we all know that better encoding leads to a better model and most algorithms cannot handle the categorical variables unless they are converted into a numerical value.

Categorical features are generally divided into 3 types: 

A. Binary: Either/or 
Examples: 

• Yes, No
• True, False

B. Ordinal: Specific ordered Groups. 
Examples: 

• low, medium, high
• cold, hot, lava Hot

C. Nominal: Unordered Groups. Examples 

• cat, dog, tiger
• pizza, burger, coke

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

Example: 

Output: 
 

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Let's examine the columns of the dataset with different types of encoding techniques. 

Code: Mapping binary features present in the dataset. 

Output:

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

Code: 

Output:

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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 the One-Hot Encoding strategy. 
One-hot encoding is processed in 2 steps:

1. Splitting of categories into different columns.
2. Put '0 for others and '1' as an indicator for the appropriate column.

Code: One-Hot encoding with Sklearn library 

Output:

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Code: One-Hot encoding with pandas 

Output:

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This method is 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: 

Output:

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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.  

Output:

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One issue with this representation (Ordinal Encoding) is that the ML algorithm would assume that the two nearby values are closer than the distinct ones.

Example of the above Problem:

Output:    

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It's looking for the most nearby ones. It assumes that "red" and "green" belong to the same category.

Code: Manually assigning ranking by using a dictionary

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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 is more categories. Imagine if you have 100 different categories. One hot encoding will create 100 different columns, But binary encoding only need 7 columns. 

Code:

Output:

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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: 

Output:

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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 is to replace a categorical value with the mean of the target variable.

Code: 

You can further drop the converted feature from your Dataframe. 

Output:

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