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Clipping

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TL;DR. A method to control outliers by capping feature values at predefined minimum and maximum thresholds.

Technical Definition

A method to control outliers by capping feature values at predefined minimum and maximum thresholds.

How it works

Clipping is a data preprocessing technique used to manage extreme values, or outliers, within a dataset. It involves setting any feature value above a specified maximum threshold to that threshold, and any value below a minimum threshold to that minimum threshold. For instance, if a feature's acceptable range is 40-60, values above 60 would be set to 60, and values below 40 would be set to 40.

Related Concepts

  • Feature Engineering — The process of creating, selecting, and transforming input variables to improve a machine learning model's performance.
  • Data Preprocessing — Transforming raw data into a format suitable for model training.

Further Reading

  • Google ML Glossary