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True Function - Python Automation and Machine Learning for ICs - - An Online Book - |
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| 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 | ||||||||
================================================================================= True function refers to the actual mathematical function that describes the relationship between the input variables and the output variable in a given problem. In supervised learning, the true function is what you aim to approximate with your machine learning model. The true function is often unknown, and your model is trained to make predictions based on the available data. Figure 3763a presents the true function together with linear model versus polynomial model with a dataset.
Figure 3763b shows the estimation and approximation errors with noisy data. The estimation error refers to the difference between the true function and the estimated function. The estimated function is the one which is obtained based on the hypothesis or model. The difference between the true function and the observed (noisy) data (blue scattered points) represents the approximation error. Both estimation error and approximation error can be positive or negative.
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