Symbolic regression: Difference between revisions

Revision as of 22:39, 26 January 2017

Revision as of 12:44, 2 June 2017

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== Difference from classical regression ==

While conventional regression techniques seek to optimize the parameters for a pre-specified model structure, symbolic regression avoids imposing a prioriprior assumptions, and instead infers the model from the data. In other words, attempts to discover both model structures and model parameters.

This approach has, of course, the disadvantage of having a much larger space to search — in fact, not only the search space in symbolic regression is infinite, but there are an infinite number of models which will perfectly fit a finite data set (provided that the model complexity isn't artificially limited). This means that it will possibly take a symbolic regression algorithm much longer to find an appropriate model and parametrization, than traditional regression techniques. This can be attenuated by limiting the set of building blocks provided to the algorithm, based on existing knowledge of the system that produced the data; but in the end, using symbolic regression is a decision that has to be balanced with how much is known about the underlying system.