DEV Community

Cover image for Underfitting and Overfitting Concepts in Machine Learning
Pranesh Chowdhury
Pranesh Chowdhury

Posted on • Edited on

2 1 2

Underfitting and Overfitting Concepts in Machine Learning

Underfitting and overfitting are issues that can occur in supervised machine learning problems. These problems involve training a model to learn a mapping from input data to output labels based on a given dataset.

Image description

Overfitting

The model shows high accuracy during the training phase but fails to show similar accuracy during the testing phase.

The model is too complex and learns not only the underlying patterns but also noise and outliers specific to the training set. While it performs exceptionally well on the training set, it struggles to generalize to new, unseen data in the testing phase.

Underfitting

The model fails to show satisfactory accuracy during the training phase.

The model is too simple and fails to capture the complexities in the data, resulting in poor performance in both the training and testing phases.

Actual Good fitting:

Training and testing overall performance is good.

Image description

Image description

Thanks for reading 💙✨

References:

https://www.superannotate.com/blog/overfitting-and-underfitting-in-machine-learning
https://www.geeksforgeeks.org/underfitting-and-overfitting-in-machine-learning/

Image of Docusign

🛠️ Bring your solution into Docusign. Reach over 1.6M customers.

Docusign is now extensible. Overcome challenges with disconnected products and inaccessible data by bringing your solutions into Docusign and publishing to 1.6M customers in the App Center.

Learn more

Top comments (0)

Image of Timescale

Timescale – the developer's data platform for modern apps, built on PostgreSQL

Timescale Cloud is PostgreSQL optimized for speed, scale, and performance. Over 3 million IoT, AI, crypto, and dev tool apps are powered by Timescale. Try it free today! No credit card required.

Try free

👋 Kindness is contagious

Please leave a ❤️ or a friendly comment on this post if you found it helpful!

Okay