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Power Real Polynomial to approximate functions: The Gradient Method
In the real world rarely a problem can be solved using just a single algorithm, more often a solution is a chain of algorithms where the output of the former is the input for the follower.
But you know that quite often machine learning algorithms return functions almost always extremely complex, and they don’t fit directly in the next step of your strategy.
In these conditions, it is really helpful the trick of the function approximation, that is, we reduce the complexity of our original model using a new easier model.
The big advantage of the function approximation is that you can impose the form of new model to describe your data.
| Polynomial approximation: here the convergence required six monomials and 70.000 iterations. The final results has obtained via "fine-tuning" and it took 5000 iterations. (In red the original function, in orange the approximation) |
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