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, the Specialist observes that many features are highly correlated with each other. This may make the model unstable. What should be done to reduce the impact of having such a large number of features?
A) Perform one-hot encoding on highly correlated features.
B) Use matrix multiplication on highly correlated features.
C) Create a new feature space using principal component analysis (PCA)
D) Apply the Pearson correlation coefficient.

Question

Quizplus · Accepted Answer

Principal Component Analysis (PCA) is a dimensionality reduction technique that can transform the original feature space into a new set of uncorrelated features, reducing the impact of multicollinearity and improving model stability.

A Machine Learning Specialist Is Building a Prediction Model for a Large