Jayita Gulati compares two popular algorithms for classification:
When working with machine learning on structured data, two algorithms often rise to the top of the shortlist: random forests and gradient boosting. Both are ensemble methods built on decision trees, but they take very different approaches to improving model accuracy. Random forests emphasize diversity by training many trees in parallel and averaging their results, while gradient boosting builds trees sequentially, each one correcting the mistakes of the last.
This article explains how each method works, their key differences, and how to decide which one best fits your project.
Click through for the explanation.
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