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Difficulty: Medium
Category: Linear Algebra & Machine Learning
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Topics: machine-learning, bias-variance, model-complexity, statistics
You are building a model to predict the next-day return of a specific stock. You know that the expected prediction error (Mean Squared Error, or MSE) can be decomposed into three components: bias squared, variance, and irreducible error. How does increasing model complexity (e.g., using higher-order polynomial features in a linear regression, or increasing the depth of a decision tree) generally affect these three components?
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