// MODEL OPTIMIZATION AND PROMPT SYNTAX TERM
Random Forest
A versatile machine learning algorithm that combines multiple decision trees to make more accurate and stable predictions.
TECHNICAL DEFINITION
An ensemble learning method that constructs a multitude of decision trees during training and outputs the class that is the mode of the classes (classification) or mean prediction (regression) of the individual trees, reducing overfitting.
BACKGROUND
A large language model (LLM) is a neural network trained on a vast amount of text for natural language processing tasks, especially language generation. LLMs can typically generate, summarize, translate and analyze text in many contexts, and are a foundational technology behind modern chatbots. Biased or inaccurate training data can make an LLM's output less reliable.
READ MORE ON WIKIPEDIASYNONYMS & ALIASES
- Ensemble trees
- forest of trees
- bagging trees
USAGE NOTE
Random Forest is widely used for both classification and regression tasks due to its robustness and good performance.