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Categorical Encoding Explorer

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Description

An interactive visualization comparing label encoding and one-hot encoding for categorical variables.

Learning Objectives

  • Understand how label encoding assigns integer values to categories
  • Learn how one-hot encoding creates binary columns for each category
  • Recognize the difference between nominal and ordinal variables
  • Identify when to use each encoding method

How to Use

  1. Select Example Dataset: Choose from Default, Iris Species, or Car Types
  2. Toggle drop_first: See how the parameter affects one-hot encoding dimensionality
  3. Add Rows: Click to add more sample data rows
  4. Compare Encodings: Examine the three tables showing original data, label encoding, and one-hot encoding

Key Concepts

Label Encoding

  • Assigns integers to categories (0, 1, 2, ...)
  • Memory-efficient (single column)
  • Suitable for ordinal variables and target labels
  • Warning: Introduces artificial ordering for nominal variables

One-Hot Encoding

  • Creates binary column for each category
  • No artificial ordering imposed
  • Required for nominal variables in most algorithms
  • Use drop_first=True to avoid multicollinearity

Interactive Features

  • Multiple Example Datasets: See encoding on different data types
  • Live Comparison: View all three representations simultaneously
  • Dimensionality Tracking: See how column count changes
  • Pros/Cons Analysis: Understand trade-offs for each method