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Understanding Multilinear Regression
Read Full Article: Understanding Multilinear Regression
Multilinear regression extends the concept of simple linear regression by incorporating multiple features, allowing the model to explore additional dimensions beyond a single line. Each new feature adds a new direction, transforming the model's output space from a line to a plane, and eventually to a hyperplane as more features are added. This expansion of the output space means that the set of reachable outputs becomes larger, which can reduce error or maintain it, as the model gains the ability to move in more directions. Understanding this concept is crucial for leveraging multilinear regression to improve model accuracy and performance.
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