Feature encoding converts categorical data into numerical formats that machine learning algorithms can understand. Different encoding methods are suitable for different types of categorical data and modeling approaches.
Our form provides four encoding options for transforming categorical data in your dataset:
Creates binary columns for each category value. Best for nominal categories with no inherent order or ranking relationship between values.
Assigns a unique integer to each category. Simple but implies an ordering which may not be appropriate for all categorical data.
Similar to Label Encoder but specifically designed for ordinal data where categories have a meaningful order or rank.
Skip encoding if your data is already properly encoded or if you only have numerical features.
To choose an encoding method in the form, simply click on one of the encoding option cards. The selected option will be highlighted with the primary color.
| Encoding Method | Best For | Example Use Case |
|---|---|---|
| One-Hot Encoder |
| Color, product type, country, or any category without an inherent order |
| Label Encoder |
| Target variables in classification, text labels where size is a concern |
| Ordinal Encoder |
| Education level, size categories (small/medium/large), satisfaction ratings |
| None |
| Already processed data, pure numerical datasets, specialized preprocessing pipelines |
One-hot encoding creates a new binary column for each category value:
| Original | Encoded (Red) | Encoded (Blue) | Encoded (Green) |
|---|---|---|---|
| Red | 1 | 0 | 0 |
| Blue | 0 | 1 | 0 |
| Green | 0 | 0 | 1 |
Label encoding assigns a unique integer to each category:
| Original | Encoded |
|---|---|
| Red | 0 |
| Blue | 1 |
| Green | 2 |
Note: This creates an implicit ordering (Red < Blue < Green) which may not be appropriate for all categorical data.
Ordinal encoding is similar to label encoding but is explicitly used for data with a natural order:
| Original | Encoded |
|---|---|
| Small | 0 |
| Medium | 1 |
| Large | 2 |
Here the ordering (Small < Medium < Large) correctly represents the inherent hierarchy in the data.
No ordinal relationship implied
Works well with most algorithms
Creates many new columns (dimensionality)
Maintains original column count
Handles high cardinality features well
May imply incorrect ordering in non-ordinal data
One-hot encoding can create too many columns when you have categories with many unique values (high cardinality). For features with more than 10-15 unique values, consider using label encoding or more advanced techniques like target encoding.