Linear Regression or Decision Trees: Which Model Should You Use?
In predictive modeling, two of the most popular algorithms are Linear Regression and Decision Trees. Both have become go-to tools for data scientists who need to build reliable, interpretable models. But which one is right for your next project? In this comprehensive guide, we’ll dive deep into the mechanics, advantages, disadvantages, and use cases for linear regression versus decision trees. By the end, you’ll have a clear understanding of when and why to choose one over the other.
Linear regression is one of the oldest and most widely used statistical modeling techniques. Its primary purpose is to model the relationship between a dependent variable \( y \) and one or more independent variables \( x_1, x_2, ..., x_n \) by fitting a linear equation to observed data.
The standard form of the multiple linear regression model is:
