05 – Feature Encoding
📘 RIDE User Manual – Panel 5: Feature Encoding
🎯 Purpose of the Panel
The Feature Encoding panel helps convert categorical variables into numerical form so they can be used in machine learning models. Users can choose between Label Encoding, One Hot Encoding, and Ordinal Encoding depending on the data's characteristics.
Recommended Reading
🧭 User Workflow
-
Upload Dataset
Option to use either:- Initial DataFrame
- DataFrame after Missing Value Imputation
-
Choose Columns & Method
- Select one or more categorical columns.
- Pick an encoding method.
-
Apply Encoding
- Transforms the selected columns using the chosen method.
- Shows:
- Resulting encoded dataset
- Column structure comparison (before vs after)
- Option to download the transformed dataset.
💻 Features Breakdown
| Feature | Description |
|---|---|
| Data Source Selection | Choose between raw or imputed data. |
| Encoding Method Selector | Label, One Hot, or Ordinal encoding. |
| Multi-column Encoding | Allows encoding multiple columns at once. |
| Before/After Comparison View | Side-by-side view of original vs new feature structure. |
| Encoded Data Preview | See the first few rows of the transformed dataset. |
| Download Encoded Dataset | Export results as CSV for later use. |
🧠 Encoding Methods & When to Use Them
| Encoding Method | Description | Best Used For | Example |
|---|---|---|---|
| Label Encoding | Assigns each unique category a number (0, 1, 2...). | Ordinal categorical data | Low → 0, Medium → 1, High → 2 |
| One Hot Encoding | Creates binary columns for each category (0 or 1). |
Nominal (unordered) data | Color → [is_red, is_blue, is_green] |
| Ordinal Encoding | Similar to Label, but requires understanding of order. | Ordinal with meaningful rank | Small → 1, Medium → 2, Large → 3 |
- Label Encoding: What is label encoding? Application of label encoder in machine learning and deep learning models.
- One Hot Encoding: What Is One Hot Encoding and How to Implement It in Python
- Ordinal Encoding: Ordinal Encoding — A Brief Explanation
🔍 Why Encoding Matters
Most machine learning algorithms require numeric inputs. Encoding ensures:
- Categorical features are interpreted correctly by models.
- No bias is introduced by incorrect ordinal assumptions.
- Models can capture class-related behavior from nominal features.