Random Forest is a machine learning method that combines many decision trees and averages their predictions, producing results that are more accurate and reliable than any single tree.
What it means in plain English
A single decision tree can be unstable and prone to overfitting. A random forest addresses this by building many trees, each on a slightly different random slice of the data and features, then combining their votes. This “wisdom of the crowd” approach cancels out individual trees’ mistakes and produces robust, accurate predictions. It is a type of ensemble learning.
Random forests are a popular, dependable choice for structured, tabular data.
A simple example
To predict whether a customer will churn, a random forest builds hundreds of decision trees on different samples of the data and combines their predictions — giving a more reliable answer than trusting any one tree alone.
Why it matters
Random forests are one of the most widely used and dependable machine learning methods for tabular data, valued for strong accuracy with little tuning. They are a go-to tool across finance, healthcare, and business analytics.
Related terms
- Decision Tree — the building block of a random forest.
- Ensemble Learning — the principle random forests use.
- Gradient Boosting — an alternative tree-based ensemble.
Frequently asked questions
What is a random forest?
It is an ensemble of many decision trees whose predictions are combined (by voting or averaging), producing a more accurate and robust result than a single tree.
Why are random forests popular?
They are accurate, resist overfitting better than single trees, handle many data types, and need little tuning — a reliable go-to for structured/tabular data.
