You can in fact literally trash these non Pareto models using the gpmodelfilter function - leaving just the Pareto front models in your population. The purple/blue circles represent models not on the Pareto front - these are usually destined for the trash compactor. An example of a typical Pareto front is shown below as green circles. These models are usually of the most interest. The trade-off surface of models ('the Pareto front') represents models that are not beaten by any other model in both predictive performance and complexity. In regression, GPTIPS considers both the model predictive performance and model complexity in an attempt to create models that perform well but are as simple as possible. Optimise your models' accuracy/simplicity ratio But if you just want to build non-linear regression models that's fine - it's all built in. GPTIPS is completely open source, written in standard MATLAB & has a pluggable architecture - it is easy to write new functions to solve your own problems with the GPTIPS Hypothesis-ML engine. You choose the models that best suit your use case and can fine tune their structure if you want. Optimises your models' accuracy-simplicity ratio (ASR) - GPTIPS automatically generates a model portfolio containing models of different levels of complexity and predictive quality. *Īutomatically identifies key predictive features even when your data is noisy and highly correlated with many superfluous features. GPTIPS builds non-linear symbolic regression models when you don't know the 'true' underlying structure. S oothes the pain of deploying - with zero dependencies - your models outside the model building environment. Aimed at ordinary scientists, engineers, analysts, students and other professionals who need/love to build models. These models are not black boxes, they look, feel and act like regular equations like this:
Perform polynomial multiplication and simplify the results, show that ( x - 1 ) ( x + 1 ) ( x 2 + x + 1 ) ( x 2 + 1 ) ( x 2 - x + 1 ) ( x 4 - x 2 + 1 ) simplifies to x 1 2 - 1.No more black boxes! Uses machine learning driven explainable AI ( XAI) to automatically learn compact, explainable and accurate non-linear equations from your data. Most mathematical expressions can be represented in different, but mathematically equivalent forms and the Symbolic Math Toolbox supports a number of operations, including factoring or expanding expressions, combining terms, rewriting or rearranging expressions, and simplification based on assumptions. The Symbolic Math Toolbox supports the Formula Manipulation and Simplification of mathematical functions.
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