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I rattled off the advantages fine, interpretability, computational efficiency, works well when the relationship is actually linear.
Start by defining linear regression and its core assumptions, then systematically discuss its advantages (e.g., simplicity, interpretability, efficiency) and limitations (e.g., sensitivity to outliers, assumption violations, inability to capture non-linear relationships). Conclude by mentioning extensions and alternatives, and relate to practical scenarios in data science.
Pro tip: Emphasize that linear regression is a foundational model and often a good baseline; showing awareness of when to use it versus more complex models demonstrates practical judgment. Also, mention that understanding its limitations is crucial for feature engineering and model selection.
Briefly define linear regression and state its key assumptions (linearity, independence, homoscedasticity, normality of errors, no multicollinearity).
List advantages such as simplicity, interpretability, computational efficiency, and solid statistical foundation.
Discuss limitations including sensitivity to outliers, assumption violations, inability to model non-linear relationships, and poor performance with complex data.
Mention extensions like regularization (Ridge, Lasso) and alternatives like polynomial regression, tree-based models, or neural networks.
Relate to real-world data science scenarios, emphasizing when linear regression is appropriate and when to consider other models.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.