Intermediate · Data
Hashing
Visual diagram · (in preparation) · Math · (in preparation) · Worked example · 3 difficulty levels.
TL;DR. A technique to group many categorical features into fewer, more manageable buckets.
Technical Definition
A technique to group many categorical features into fewer, more manageable buckets.
How it works
Hashing is a method used in machine learning to handle categorical features, especially when there are a very large number of unique categories. Instead of creating a separate bucket for each category, hashing maps multiple categories into a smaller, fixed number of buckets. This is useful when only a small fraction of possible categories actually appear in the dataset, reducing memory usage and complexity.
Related Concepts
- Feature Engineering — The process of creating, selecting, and transforming input variables to improve a machine learning model's performance.
- Dimensionality reduction — The process of reducing the number of features or variables in a dataset while retaining essential information.
- Categorical data — Data that represents categories or labels, where each data point belongs to one specific group from a defined set of possibilities.