Question

A data scientist is preparing features for a linear regression model that predicts house prices. During EDA, they compute a Pearson correlation matrix and notice that 'total_square_feet' and 'number_of_rooms' have a correlation coefficient of 0.94 with each other, while each correlates around 0.60 with the target price. What is the most appropriate action to take before training the linear model?

Accepted Answer

Drop one of the two highly correlated features (or combine them) to reduce multicollinearity between predictors

Answer

Drop both features because their correlation with the target is only moderate (0.60)

Answer

Keep both features unchanged, since high correlation between predictors improves a linear model's accuracy

Answer

Apply StandardScaler to both features, which will eliminate the correlation between them