R-squared › Limitations
Michael BrenndoerferApril 4, 20251 min read
Machine Learning from ScratchAll chapters Part 3 of 9Foundations
R-squared › LimitationsOne section · Sections in this chapter10 / 12 ↓
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- R-squared does not indicate whether a regression model is appropriate.
- It can be artificially high for models with many predictors (use adjusted R-squared for multiple regression).
- A high R-squared does not imply causation.
- R-squared doesn't measure prediction accuracy on new data.
- It can be misleading with nonlinear relationships or outliers.
R-squared is a useful first check for model fit, but always consider it alongside other diagnostics and domain knowledge.