Random Forest is an ensemble learning method for classification, regression, and other tasks that operates by constructing a multitude of decision trees at training time. It outputs the mode of the classes (classification) or mean prediction (regression) of the individual trees.
Random Forest in Practice
Random Forests are known for their high accuracy, robustness, and ease of use. They handle both categorical and continuous data and provide a measure of feature importance. They are less likely to overfit than a single decision tree.
Download this guide to delve into the most common LLM security risks and ways to mitigate them.
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.