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Modeling a binary yes/no output
Binary classification attempts to predict a variable that has only two possible outcomes - for example, true or false, or buy or don't buy. This post describes how Eureqa can be used to model a boolean decision or classification value. Binary classification is also one of the most widely studied problems in machine learning, and there are many optimized approaches for prediction (e.g. neureal nets, support vector machine, etc). Using Eureqa for classification (or symbolic regression in general) has a few advantages: finding models requires less data models can extrapolate extremely well resulting models are simple to analyze, refit, and reuse the structure of the models gives insight into the classification problem The last point is the most important in my opinion - not only can you predict but you can also learn something about how the classification works, as in the example below.
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Eureqa | Cornell Computational Synthesis Laboratory
Eur eq a (pronounced "eureka") is a software tool for detecting equations and hidden mathematical relationships in your data. Its goal is to identify the simplest mathematical formulas which could describe the underlying mechanisms that produced the data. Eureqa is free to download and use.