Polynomial models in ML

Polynomial Models in ML
- Python Automation and Machine Learning for ICs -
- An Online Book -

Python Automation and Machine Learning for ICs http://www.globalsino.com/ICs/

Chapter/Index: Introduction | A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | Q | R | S | T | U | V | W | X | Y | Z | Appendix

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Higher-order polynomial models, such as fifth-order polynomials, are capable of fitting training data very closely, which can result in a very low training set error as shown in Figure 3793. However, they are prone to overfitting. Overfit models memorize the training data and may not generalize well to unseen data. The low training error may not reflect the model's performance on new, unseen data, and it could lead to poor generalization.

Polynomial regression with different orders

(a)

Polynomial regression with different orders

(b)

Figure 3793. Polynomial regression with different orders: (a) Polynomial regressions, and (b) Mean squared error (Code).

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