Linear Regression as a Projection Problem (Part 2)
This article explores the vector view of least squares in linear regression, explaining how projections can be used to make predictions.
Why it matters
Understanding the projection-based view of linear regression provides deeper insights into the underlying mathematics and can help practitioners better interpret and apply the technique.
Key Points
- 1Linear regression can be viewed as a projection problem
- 2The goal is to find the projection of the target variable onto the feature space
- 3The projection vector is the regression coefficients, which minimize the sum of squared residuals
Details
The article discusses the vector perspective of linear regression, where the goal is to find the projection of the target variable onto the feature space. This projection vector corresponds to the regression coefficients, which minimize the sum of squared residuals. The author explains how this projection-based approach can be used to make predictions, highlighting the connection between projections and predictions in the context of linear regression.
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