# Bucketization

> Converting a single feature into multiple binary features called **buckets** or **bins**, typically based on a value range. The chopped feature is typically a continuous feature.[^1]

Converting a single [feature](https://wiki.g15e.com/pages/Feature%20(machine%20learning.txt)) into multiple binary features called **buckets** or **bins**, typically based on a value range. The chopped feature is typically a [continuous feature](https://wiki.g15e.com/pages/Continuous%20feature.txt).[^1]

For example, instead of representing temperature as a single continuous floating-point feature, you could chop ranges of temperatures into discrete buckets, such as:

- $<= 10$ degrees Celsius would be the "cold" bucket.
- $11 - 24$ degrees Celsius would be the "temperate" bucket.
- $> = 25$ degrees Celsius would be the "warm" bucket.

The [model](https://wiki.g15e.com/pages/Model%20(machine%20learning.txt)) will treat every value in the same bucket identically. For example, the values `13` and `22` are both in the temperate bucket, so the model treats the two values identically.

## Footnotes

[^1]: https://developers.google.com/machine-learning/glossary#bucketing