Random Forest

Category: science

An ensemble learning method that constructs multiple "decision trees" and merges them to get a more accurate prediction.

A single tree can be shaky. A forest of hundreds is solid. By letting each "tree" make a guess and taking the average result, the model stays incredibly accurate even if one of the trees is wrong. It is one of the most reliable tools for structured business data.

Common Examples

• We used a Random Forest ensemble to categorize the insurance leads, as it outperformed a single neural network on tabular data.
• Random Forest is a highly effective, production-grade tool that offers great interpretability for business stakeholders.