Specificity
Specificity in machine learning measures the proportion of actual negatives correctly identified by the model (True Negatives) out of all actual negatives (True Negatives + False Positives). It's also known as the True Negative Rate.
Importance of Specificity
Specificity is critical in scenarios where the cost of false positives is high. It is often used alongside sensitivity to provide a comprehensive view of a model's performance in classification tasks.
Download this guide to delve into the most common LLM security risks and ways to mitigate them.
untouchable mode.
Lakera Guard protects your LLM applications from cybersecurity risks with a single line of code. Get started in minutes. Become stronger every day.
Several people are typing about AI/ML security. 
Come join us and 1000+ others in a chat that’s thoroughly SFW.
