Extremely Wide One-Hot Encoding: The Curse of Dimensionality

This visualization dramatically demonstrates the "curse of dimensionality" — a critical problem that occurs when one-hot encoding categorical variables with many unique values. Watch as a simple dataset with just 4 columns expands to over 150 columns, creating an extremely sparse matrix.

The Curse of Dimensionality Problem

One-hot encoding is a standard technique for handling categorical variables in machine learning. However, when a categorical feature has many possible values (like product categories, ZIP codes, or user IDs), one-hot encoding creates an explosion in dimensionality:

How This Visualization Works

The animation shows a progression through increasingly extreme one-hot encoding:

  1. Starting with a simple 4-column dataset (id, customer, category, purchase)
  2. Moving through stages of encoding with more and more categorical columns
  3. Ending with 150+ columns, most containing only zeros (an extremely sparse matrix)

Watch how the dataset width grows dramatically while becoming increasingly sparse. This visually demonstrates why handling high-cardinality categorical features often requires techniques beyond simple one-hot encoding.

One-Hot Encoding: Extreme Width Visualization

Original dataset: 4 columns
Dataset Structure
4 columns total
id
customer
category
purchase
Dataset Expansion Visualization
4 cols
+0 one-hot columns
wider than the original dataset!
With 240 categories, the dataset is now extremely sparse — each row contains just 1 non-zero value across 240 encoded columns

Alternatives to One-Hot Encoding for High-Cardinality Features

When dealing with categorical features that have many unique values, consider these alternatives:

These techniques can significantly reduce dimensionality while preserving most of the information in high-cardinality categorical features.