Overfitting vs. Underfitting (Pilot)

Type of course:

Digital learning, Lesson

Language:

Duration:

5 minutes

Proficiency:

Beginner

Target:

Professionals

SUMMARY

Throughout this lesson, you will learn about two common problems in model fitting: overfitting and underfitting. This lesson will guide you on how to identify these issues and apply strategies to avoid them. Learning to identify and avoid overfitting and underfitting is crucial for building accurate, reliable, and well-generalized machine learning models.

In this lesson, we’ll explore:

The consequences of overfitting and underfitting
How to identify them
How to avoid them

About The Author

Thi Hong Nhung Dang (Nhung) is an engineer in AI at the Grenoble Institute of Technology (Grenoble INP), France. She approaches manufacturing challenges by not only leveraging data but also considering human factors, economic perspectives, and the broader context to deliver well-rounded machine learning solutions.

Learning outcomes

By the end of this lesson, learners will be able to identify overfitting in machine learning models.
By the end of this lesson, learners will be able to identify underfitting in machine learning models.
By the end of this lesson, learners will be able to explain the consequences of overfitting and underfitting, as well as apply techniques to avoid them in machine learning models.