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A data analyst is examining the correlation matrix of a new data set to identify issues that could adversely impact model performance. Which of the following is the analyst most likely checking for?
Correct Answer: B
# Multicollinearity occurs when independent variables are highly correlated with each other. This can distort coefficient estimates and reduce model interpretability. A correlation matrix is the primary tool used to detect it. Why the other options are incorrect: * A & C: Under/oversampling relate to class imbalance, not variable correlation. * D: Overfitting is related to model complexity, not directly observable via a correlation matrix. Official References: * CompTIA DataX (DY0-001) Study Guide - Section 3.2:"Correlation matrices are used to detect multicollinearity - high correlations among predictors that may destabilize models."
