What is label encoding?

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Label encoding is a data preprocessing technique used to convert categorical values (non-numeric labels) into numeric form so that machine learning algorithms can process them.

🔹 How it works:

• Each unique category is assigned a unique integer value.

• Example: For a feature “Color” with categories:

  • Red → 0

  • Green → 1

  • Blue → 2

So, if your dataset had [Red, Blue, Green, Red], after label encoding it becomes [0, 2, 1, 0].

🔹 Why it’s used:

• Many ML algorithms (like linear regression, logistic regression, neural networks) require numerical input.

• Label encoding provides a compact and efficient way to transform categorical features into numbers.

🔹 Limitations:

• Implied Ordinal Relationship: The encoded numbers may suggest a natural order or ranking where none exists.

  • Example: Encoding Red=0, Green=1, Blue=2 implies Blue > Green > Red, which may mislead the model.

• This can negatively impact algorithms that rely on distance or order (like KNN, linear regression).

• Works better when the categorical feature is ordinal (where order matters, e.g., Small=0, Medium=1, Large=2).

🔹 Key Difference from One-Hot Encoding

• Label Encoding → Assigns integers (compact but may add false order).

• One-Hot Encoding → Creates binary vectors (avoids false order but increases dimensionality).

✅ In short: Label encoding transforms categories into integers, making them usable for ML models. It’s simple and space-efficient but must be used carefully to avoid introducing unintended ordinal relationships.

Read more:

What is PCA (Principal Component Analysis)?

What is one-hot encoding?

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