What a random forest is
A random forest is a machine learning model that combines many decision trees to make predictions. Instead of relying on a single tree, it builds a collection, or forest, of them and combines their results. By pooling the predictions of many trees, a random forest typically produces more accurate and reliable results than any individual tree would on its own. This combining of models is its defining idea.
The power of combining models
A random forest is an example of an ensemble method, which means it combines multiple models to improve performance. A single decision tree can be sensitive to the particular data it was built from and may make mistakes. By averaging or voting across many trees, a random forest smooths out individual errors, leading to predictions that are generally more robust and dependable than those of one tree.
Why randomness helps
The random in random forest refers to the variety introduced among the trees. Each tree is built with some randomness, so the trees differ from one another rather than being identical. This variety is important: a group of diverse trees that each see things slightly differently tends to make better combined decisions than a group of identical ones. The randomness helps the forest avoid relying too heavily on any single pattern.
How a random forest predicts
To make a prediction, a random forest runs the input through all of its trees and combines their individual predictions into a final answer. For choosing a category, this often means taking a majority vote among the trees; for predicting a number, it can mean averaging their outputs. This pooling of many perspectives is what gives the random forest its strength.
Where random forests are used
Random forests are widely used because they are accurate, robust, and relatively easy to apply to many kinds of problems. They are used for predicting categories and numerical values across fields such as finance, medicine, and technology. Their strong performance without requiring extensive tuning has made random forests a popular and trusted tool in machine learning.
Why it matters
Random forests show how combining many simple models can produce a stronger one. Understanding what a random forest is - an ensemble of diverse decision trees whose predictions are combined - clarifies a powerful and widely used machine learning method, and the general idea of ensembles. For anyone interested in AI, it is a valuable concept.
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See also: What is a decision tree? Explained, What is machine learning? Explained for beginners.
Sources
Published date reflects the original event date (2023-12-05). This article is original Skillo editorial written from the sources above; facts were verified in September 2026.
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