You are building a regression model to predict residential sale prices for a real estate marketplace. Early experiments show that simple linear models miss important nonlinear effects, while more flexible models fit the training data very well but behave inconsistently on unseen properties.
How would you explain the bias-variance tradeoff in this setting, and how would you use that concept to choose model complexity, features, and regularization for a production-ready price prediction model?
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