One-Hot Encoding vs. Label Encoding: When to Use Them

August 03, 2025

One-Hot Encoding vs. Label Encoding are both techniques used to convert categorical data into numerical format, but they serve different purposes and are used in different contexts.

🔹 Label Encoding

What it does: Assigns each unique category a unique integer value.

Category Encoded

Red 0

Green 1

Blue 2

✅ When to Use Label Encoding

When the categorical variable is ordinal (i.e., the categories have a meaningful order, like Low, Medium, High).

When you have a tree-based model (e.g., decision trees, random forests, XGBoost) — these can typically handle label-encoded data well.

❌ Avoid Label Encoding When:

The categories are nominal (no intrinsic order), and you're using models that assume numerical relationships (e.g., linear regression, logistic regression, SVM). In such cases, Label Encoding may mislead the model into thinking one value is "greater" than another.

🔹 One-Hot Encoding

What it does: Creates binary columns for each category.

Category Red Green Blue

Red 1 0 0

Green 0 1 0

✅ When to Use One-Hot Encoding

When the variable is nominal (e.g., color, city names, gender) and there's no meaningful order.

When using linear models, neural networks, or any model that assumes numerical continuity or distance.

❌ Avoid One-Hot Encoding When:

The categorical variable has high cardinality (e.g., hundreds or thousands of categories), which can lead to a large, sparse dataset and increased computational cost.

🔁 Summary Table

Feature Label Encoding One-Hot Encoding

Type of data Ordinal Nominal

Output Single column Multiple columns

Introduces order? Yes No

Suitable for tree models Yes Yes

Suitable for linear models Risky if nominal Yes

Handles high cardinality Better Not ideal

⚖️ Rule of Thumb

Use Label Encoding for ordinal data or when using tree-based models.

Use One-Hot Encoding for nominal data or with linear and distance-based models.

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