MLS-C01 Question #15: Real Exam Question with Answer & Explanation

Question

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?

Options

• APerform one-hot encoding on highly correlated features.
• BUse matrix multiplication on highly correlated features.
• CCreate a new feature space using principal component analysis (PCA)
• DApply the Pearson correlation coefficient.

Explanation

A linear model is being built with many highly correlated features, which can lead to model instability.

Common mistakes.

• A. One-hot encoding is used for categorical features, not for reducing correlation or dimensionality in continuous, highly correlated features.
• B. Matrix multiplication is a mathematical operation, not a technique for reducing correlation or dimensionality in features to improve model stability.
• D. Applying the Pearson correlation coefficient is a method to measure correlation, not to reduce its impact or the number of features.

Concept tested. Dimensionality reduction for multicollinearity using PCA

Reference. https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html

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