This page summarizes the results of the postprocessing notebooks found in this folder.
Results are summarized over datasets.
We analyze two types of problems:
Black-box Regression Problems: problems for which the ground-truth model is not known/ not sought. Includes a mix of real-world and synthetic datasets from PMLB. 122 total.
Ground-truth Regression Problems: problems for which the ground-truth model known. Includes datasets from the Feynman Symbolic Regression Database and dynamical systems from the ODE-Strogatz Database. 130 total.
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Considering the accuracy and simplicity of models simultaneously, this figure illustrates the trade-offs made by each method. Methods lower and to the left produce models with better trade-offs between accuracy and simplicity.
How often a method finds a model symbolically equivalent to the ground-truth process
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How often a method finds a model with test set R2>0.999
