Looking for a Picture that shows Statistical Models "Learning" from Data I am trying to find a picture (e.g. on Google Images) that illustrates the following concept:

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*The existence of a "true ideal" function that by definition is never fully observable


*The process of a statistical model trying to "learn" this "true ideal" function based on data points from this "true ideal" function
The closest thing I could find was the following picture:

This picture clearly shows observations from the "true ideal" (and fundamentally unobservable) function and an attempt to "learn" this "true ideal" function based on limited observations.
Does anyone else have any other recommendations for pictures that illustrate this concept?
Reference:

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*https://www.pushpakjagtap.com/project/data/

*https://blog.dominodatalab.com/fitting-gaussian-process-models-python

I would put emphasis on "the process of a statistical model trying to "learn" this "true ideal" function". This is clearly what gradient descent does.
Consider a simple linear regression: yet there is an analytical solution for its coefficients ($\hat \beta = (X^T X)^{-1} X^T y$), coefficients are also often found with gradient descent. So, the optimization method starts in some "start point" and then moves regression line to minimize loss function and improve fit of model to data.
See example of visualisation: Scipython blog You can see there the "process" of fitting coefficients to data, as the regression line moves closer and closer to the true function at each iteration of the gradient descent.
