You work as a machine learning specialist at a mining and minerals company. Your company has asked you to build a model that predicts the efficacy of a given drilling site. Your model training dataset has a large number of features. For your modeling exploration, you have chosen to use regression models, such as linear regression and logistic regression. During exploratory data analysis, you notice a high correlation between many features that you believe will make your model unstable. How can you address the problem of having too many highly correlated features?

Answer options:

A.Use a Cramer’s V correlation coefficient.
B.Use Principal Component Analysis to reduce the dimensionality of the dataset.
C.Modify highly correlated features using vector multiplication.
D.Modify highly correlated features using a Spearman correlation coefficient.