A decision tree is one of the most intuitive methods in machine learning: it makes a prediction by asking a series of yes-or-no questions, like a flowchart. To decide whether a loan applicant is risky, a tree might ask “Is income above X?”, then “Have they missed a payment?”, and so on, until it reaches a leaf that gives an answer. Because each path is a chain of plain questions, a single tree is easy for a person to read and explain, which is a large part of its appeal in regulated industries.
A single tree is also fragile. It tends to memorize the quirks of its training data, a problem called overfitting, so its predictions swing wildly when the data changes slightly. The fix that made trees dominant is the random forest, introduced by Leo Breiman in his 2001 paper “Random Forests,” published in the journal Machine Learning. A random forest grows hundreds of trees, each trained on a random sample of the data and a random subset of the available questions, then averages their votes. Breiman showed that this combination is far more accurate and stable than any single tree while resisting overfitting. A related practice, cross-validation, is used to estimate how well such models will perform on data they have not seen, by repeatedly holding out part of the data for testing.
Why business readers should care: most real corporate data is tabular, rows and columns in spreadsheets and databases, covering customers, transactions, sensors, and claims. On this kind of data, tree-based methods routinely match or beat far more complex neural networks while training in seconds, running cheaply, and offering some insight into which factors mattered. They are the quiet default behind credit scoring, churn prediction, fraud detection, and demand forecasting in countless companies.
The honest limits are real. Trees do not handle raw images, audio, or free text well, where deep learning dominates. They can still overfit if grown carelessly, and a forest of hundreds of trees is harder to fully explain than a single one. But for the structured data that runs most businesses, decision trees and random forests remain the practical, proven first choice.