Beginner · Data
Imputation
Visual diagram · (in preparation) · Math · (in preparation) · Worked example · 3 difficulty levels.
TL;DR. The process of replacing missing data points with substituted values.
Technical Definition
The process of replacing missing data points with substituted values.
How it works
Imputation is a technique used to handle missing values within a dataset. It involves estimating and replacing these missing data points with plausible substituted values. This process is crucial for maintaining the integrity of the data and ensuring that analyses or model training are not significantly skewed by incomplete information.
Related Concepts
- Feature Engineering — The process of creating, selecting, and transforming input variables to improve a machine learning model's performance.
- Dataset — An organized collection of examples used to train, validate, or test a model.
- Data Cleaning — Identifying and fixing or removing errors, inconsistencies, and duplicates in a dataset.