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Table 1
Least squares polynomial fitting/trend surface: Pros and cons.
| Least squares polynomial fitting | |
| | |
| Pros | Simple and intuitive |
| Fast to compute | |
| | |
| Cons | Usually only able to capture broad features (underfitting) |
| Increasing the order of polynomials does not improve and generally degrades accuracy (overfitting) | |
| High-order polynomials generate numerical issues (rounding errors, overflow, etc.) | |
| High sensitivity to outliers and fitting errors | |
| Local changes propagate to the whole polynomial surface | |
| No estimation of interpolation errors (deterministic) | |
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