A Gentle Introduction to Concept Drift in Machine Learning
Last Updated on August 12, 2019
Data can change over time. This can result in poor and degrading predictive performance in predictive models that assume a static relationship between input and output variables.
This problem of the changing underlying relationships in the data is called concept drift in the field of machine learning.
In this post, you will discover the problem of concept drift and ways to you may be able to address it in your own predictive modeling problems.
After completing this post, you will know:
- The problem of data changing over time.
- What is concept drift and how it is defined.
- How to handle concept drift in your own predictive modeling problems.
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A Gentle Introduction to Concept Drift in Machine Learning
Photo by Joe Cleere, some rights reserved.
Overview
This post is divided into 3 parts; they are: