- Pay attention to R^2 and the curve behavior. 

- More "wiggly" implies a higher degree polynomial, therefore it will reach a point where all points will pass through the curve. 
 
- More linear is possibly a underfit case but may not




Overfitting-Underfit Example:

- There are few parameters to work with the model. 
- Poor variance explanation or low R-squared
- Variance/Bias trade results in high bias-low variance. 

University of Rochester

- These concepts are related to model fitting within or outside Bayesian Statistics

- Direct relation to the number of parameters used to build a model

- There is a preference to use simpler models (Ockham’s razor), then we should ask when is too simple?

- There is a measure for model fitting, R-squared, which is not always an indicator of a good fit.


Underfitting vs Overfitting

McElreath, R. (2020). Statistical Rethinking: A Bayesian Course with Examples in R and STAN (2nd ed.). Chapman and Hall/CRC.

Statistical Rethinking: A Bayesian Course with Examples in R and STAN (2nd edition)

Overfitting occurs when a model performs significantly better on its training data than on validation or holdout data, resulting in a large generalization gap. This indicates that the model is overly complex and has begun to memorize noise rather than learning generalizable patterns. However, severe overfitting is not always inherently detrimental in deep learning; the best predictive models often exhibit this behavior. The primary goal is to drive the overall generalization error lower, meaning the gap is only problematic if it obstructs that goal.

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