Web Reference: R-squared is a goodness-of-fit measure for linear regression models. This statistic indicates the percentage of the variance in the dependent variable that the independent variables explain collectively. R-squared measures the strength of the relationship between your model and the dependent variable on a convenient 0 – 100% scale. While the Coefficient of Determination, or R-squared (R 2 R2), gives us a handy percentage representing the proportion of variance explained by our model, it's important to understand its limitations. Relying solely on R 2 R2 can sometimes paint an incomplete or even misleading picture of your regression model's performance. R-squared Can Be Artificially Inflated One significant issue is that ... Feb 5, 2026 · To better understand when an R-squared value might be too high, or simply misleading, consider two example scenarios: Example 1: High Training R², Poor Generalization This is a common overfitting situation. Suppose we trained a regression model to predict house prices using a dataset with a few thousand instances.
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