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Continuous feature

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TL;DR. A numerical feature that can take any value within a given range, such as temperature or weight, rather than just specific discrete values.

Technical Definition

A numerical feature that can take any value within a given range, such as temperature or weight, rather than just specific discrete values.

How it works

A continuous feature is a type of input variable in a dataset that can assume any value within a real number range, often represented as floating-point numbers. Examples include measurements like height, weight, temperature, or time. This is contrasted with discrete features, which can only take on a finite or countable number of distinct values, like the number of rooms in a house or a rating on a scale.

Related Concepts

  • Feature Engineering — The process of creating, selecting, and transforming input variables to improve a machine learning model's performance.
  • Discrete feature — A feature that can only take on a finite, countable number of distinct values.

Further Reading

  • Google ML Glossary