Cookie Consent
Hi, this website uses essential cookies to ensure its proper operation and tracking cookies to understand how you interact with it. The latter will be set only after consent.
Read our Privacy Policy

Regularization Rate

Regularization Rate in machine learning is a hyperparameter that determines the strength of regularization applied to a model. It balances the original loss function with the regularization term, influencing how much the model prioritizes simplicity over fitting the training data perfectly.

Balancing Act: Regularization Rate

The choice of regularization rate is crucial: too high a rate can lead to underfitting, where the model is too simple to capture the underlying pattern. Conversely, too low a rate may lead to overfitting. The optimal rate often requires experimentation and may vary depending on the specific dataset and model used.

Lakera LLM Security Playbook
Learn how to protect against the most common LLM vulnerabilities

Download this guide to delve into the most common LLM security risks and ways to mitigate them.

Related terms
untouchable mode.
Get started for free.

Lakera Guard protects your LLM applications from cybersecurity risks with a single line of code. Get started in minutes. Become stronger every day.

Join our Slack Community.

Several people are typing about AI/ML security. 
Come join us and 1000+ others in a chat that’s thoroughly SFW.