Encoding in Machine Learning: One-Hot Encoding vs. Label Encoding

When working with machine learning, one of the very first challenges everyone encounters and will encounter is dealing with categorical data. Machine learning algorithms work with numbers, not text, so values like "Red", "Apple", or "xyz" must be converted into a numerical format. This process is called encoding.

What is Encoding?

Encoding is the process of converting text-based categorical data into numerical format, which is important for machine learning algorithms/models to process it.

For example, consider the following datasets:

A machine learning model cannot directly understand these text values. Encoding transforms them into numbers while preserving useful information.

Why Do we need Encoding?

Most machine learning algorithms perform mathematical operations such as calculating values, probabilities, distances, or gradients. Since these operations require numerical input, categorical input values must first be represented as numbers.

Without encoding, many models will produce an error or fail to learn meaningful patterns.

Types of Categorical Data

Before choosing an encoding technique, it's important to identify the type of categorical features.

1. Nominal Data

Nominal categories have no natural orders. They don't follow any orders in and out.

Examples: Colours, Countries, Animal species, Car brands

There is no logical ordering among these values.

2. Ordinal Data

Ordinal categories do follow a order. These categories have a meaningful and logical order.

Examples:

• Small -> Medium -> Large

• Low -> Medium -> High

• Poor -> Fair -> Good -> Excellent

The order matters, even if the differences between categories are not equal.

Types of Encoding

Type of Encoding matters for different types of categorical data. Here the two types of encoding come in use.

Label Encoding

Label Encoding is a type of encoding which assigns a unique integer to each category.

In the above encoded dataset you can clearly see each fruit has a different integer assigned.

How to do it using Python?

Advantages of Label Encoding

1. Simple to implement.
2. Memory efficient.
3. Suitable for ordinal data.
4. Fast preprocessing.

Disadvantages of Label Encoding

Things which have advantages, they have disadvantages too.
The encoded values introduces an artificial order.
For example:
Apple = 0
Banana = 1
Orange = 2

A model might assume that Orange > Banana > Apple. For nominal data, this assumption is incorrect and may reduce model performance.

One-Hot Encoding

One-Hot Encoding creates a new binary column for each category.

Original Data:
Color
Red
Blue
Green

After One-Hot Encoding:

Red Blue Green
1 0 0
0 1 0
0 0 1

Each category gets its own feature, eliminating any implied ordering.

How to do this using Python?

Advantages of One-Hot Encoding

1. Prevents false ordering between categories.
2. Works well for nominal features.
3. Widely supported by machine learning libraries.
4. Often improves model performance for linear models and distance based algorithms.

Disadvantages of One-Hot Encoding

1. Increases the number of features.
2. Can create sparse datasets.
3. Becomes inefficient for columns with hundreds or thousands of unique values.

When should We Use Each Encoding?

We use Label Encoding when the categories have a natural order and want a compact representation.

Examples:

1. Education Level
2. Product Size

We use One-Hot Encoding when categories are unordered and want to avoid introducing artificial relationships.

Examples:

1. Country
2. Colour
3. Gender
4. Department

Practical Tips

• Always understand the meaning of the categorical features and datasets before applying encoding.

• Avoid Label Encoding for nominal features unless your model can handle categorical variables appropriately.

Conclusion

Encoding is a major and essential preprocessing step in machine learning. Choosing the appropriate encoding technique can significantly impact model performance.

• Label Encoding is simple and effective for ordinal data.

• One-Hot Encoding is generally the preferred choice for nominal data because it avoids introducing false relationships between categories.

As a rule of thumb:
If the categories have an order, use Label Encoding. If they don't, use One-Hot Encoding.