Unlocking Categorical Data: When to Use One-Hot Encoding and Label Encoding?

Introduction:

In machine learning, a common question arises: how do we use and represent categorical features? How can we convert them into numerical features that algorithms can understand and process? When do we use label encoding, and when is one-hot encoding the better choice? This blog post aims to provide a clear understanding of these concepts and their applications.

Why Encoding?

Encoding is a technique that transforms categorical variables, which are qualitative in nature, into numerical vectors. This allows machine learning algorithms to understand and process them effectively. Categorical variables can be either:

Ordinal: These values have an inherent order, like ratings (Very Good, Good, Average, Bad, Very Bad).

Nominal: These values have no intrinsic order, like colors (Red, Green, Blue, Yellow).

How to do encoding? 

The most widely used encoding techniques are: 

1. Label Encoding, 

2. One Hot Encoding.

Label encoding: This method assigns a unique integer to each category, typically based on alphabetical order. 

Imagine you have a dataset about clothing items:

Type: Shirt, Pants, Dress.

Price: Numerical value.

We use 'Type' feature to predict the price. However, the "Type" feature is categorical and need to be encoded before feeding it into a machine learning model.

Fig: Label encoding for 'Type' feature.

In this scenario, computer might learn something like this:

Dress < Pants < Shirt. 

😞Label encoding can create an issue where the model might interpret categories with higher numerical values as inherently "better".

This is why, we go for One Hot Encoding.

One Hot Encoding:

This method creates a separate binary feature for each unique category. A value of 1 indicates the item belongs to that category, while 0 indicates it doesn't.

Fig: One Hot Encoding for 'Type' feature.

Here, dummy variable trap leads to multicollinearity issue. So, we should drop one variable from the generated dummy variables. 

Points to remember:

• We can use label encoding, if the variable is ordinal. Mostly, Label encoding is used for target(dependent) variable.
• We can use one hot encoding for nominal variable.

Python Programming:

Conclusion:

In conclusion, Label Encoding and One-Hot Encoding are fundamental techniques for preprocessing categorical variables in machine learning. While Label Encoding assigns unique integer labels to categories, One-Hot Encoding creates binary columns to represent each category independently. Understanding the differences and applications of these encoding techniques is crucial for effective feature engineering and model building in machine learning projects.