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A data scientist is performing a linear regression and wants to construct a model that explains the most variation in the data. Which of the following should the data scientist maximize when evaluating the regression performance metrics?
Correct Answer: B
# R² (coefficient of determination) quantifies how much of the variance in the dependent variable is explained by the model. A higher R² means a better fit to the data, making it the metric to maximize for explanatory power in regression analysis. Why the other options are incorrect: * A: Accuracy is used in classification, not regression. * C: p-values test statistical significance of coefficients, not overall model fit. * D: AUC (Area Under the Curve) applies to classification models, not regression. Official References: * CompTIA DataX (DY0-001) Study Guide - Section 3.2:"R² is a regression performance metric indicating the proportion of variance explained by the independent variables."
