A Machine Learning Specialist is building a prediction model for a large number of features using linear models, such as linear regression and logistic regression. During exploratory data analysis…

One-hot encoding is used for categorical features, not for reducing correlation or dimensionality in continuous, highly correlated features.

Matrix multiplication is a mathematical operation, not a technique for reducing correlation or dimensionality in features to improve model stability.

Principal Component Analysis (PCA) is a dimensionality reduction technique that transforms a set of correlated features into a smaller set of uncorrelated features called principal components. By creating a new feature space with these uncorrelated components, PCA effectively addresses multicollinearity and reduces the impact of a large number of correlated features on model stability.

Applying the Pearson correlation coefficient is a method to measure correlation, not to reduce its impact or the number of features.