![]() These equations breed at a frankly indecent rate. This is because it uses a biologically inspired algorithm that loosely mimics the processes of biological evolution in populations. In symbolic regression the form(s) of f is determined automatically by GPTIPS.Ī key concept is that the GPTIPS machine learning algorithm does not build a single model equation, rather it builds a library (sometimes called a population) of f models. In most practical cases there will be many f's that will minimise E. ![]() The typical aim is to choose an f that minimises, in some sense, the size of E. E is the error (residual) - the difference between the observed value of y and the model's prediction of y. a symbolic non-linear function (or a collection of non-linear functions). These are things you know and want to use to predict y. , x N are feature (input/predictor) variables. Where y is an output/response variable (the thing you are trying to predict) and x 1. ![]() Non-linear regression models are typically of the form Hypothesis-ML generates the models and symXAI lets you analyse, interpret, visualise and export them. That is, to allow you to automatically discover and interpret empirical symbolic non-linear regression models from data. GPTIPS provides a stack of additional functions to help you do this (referred to collectively as the sym-XAI module). The most popular use case use of GPTIPS is to perform explainable symbolic non-linear regression. This generates rules/models/hypotheses in the form of multiple trees. GPTIPS is built around a MGGP engine for MATLAB ( hypothesis-ML).
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