Google Professional Machine Learning Engineer
Get started today
Ultimate access to all questions.
Ultimate access to all questions.
In the context of developing a Linear Regression model for a Financial Institution, you identify multicollinearity among several independent variables, which adversely affects the model's performance. A seasoned Data Scientist recommends specific techniques to mitigate this issue, considering the institution's need for scalability, interpretability, and compliance with financial regulations. Which three techniques or algorithms would the Data Scientist most likely recommend to address multicollinearity effectively? (Choose three)
Explanation:
Multicollinearity among independent variables compromises the reliability of classical linear regression models. Principal Component Analysis (PCA) and Partial Least Squares Regression (PLSR) are recommended because they transform the original variables into a new set of uncorrelated variables, thereby addressing multicollinearity. PCA achieves this by reducing dimensionality while preserving variance, whereas PLSR projects the variables into a new space using a combination of the dependent and independent variables. Multivariate Multiple Regression is also advised as it allows for the simultaneous analysis of multiple dependent variables, providing insights into how variables collectively respond to changes. Options C and D, while useful in other contexts, do not directly address multicollinearity. Ridge Regression (D) and Lasso Regression (F) are techniques for regularization that can indirectly help with multicollinearity but are not primarily focused on eliminating it.