What should be done to reduce the impact of having such a large number of features?
Perform one-hot encoding on highly correlated features.
Use matrix multiplication on highly correlated features.
Create a new feature space using principal component analysis (PCA)
Apply the Pearson correlation coefficient.
Explanations:
One-hot encoding is used for categorical variables, not for handling correlation between continuous features.
Matrix multiplication is not a method for reducing multicollinearity or handling correlated features.
PCA transforms the features into a new orthogonal space, reducing correlation and potentially improving model stability.
The Pearson correlation coefficient measures the linear relationship between two variables but does not address multicollinearity in modeling.
I assess that the answer is:
Create a new feature space using principal component analysis (PCA)