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gplearn implements Genetic Programming in Python, with a scikit-learn-inspired and compatible API. While Genetic Programming (GP) can be used to perform a very wide variety of tasks, gplearn is purposefully constrained to solving symbolic regression problems. This is motivated by the scikit-learn ethos, of having powerful estimators that are straightforward to implement. Symbolic regression is a machine learning technique that aims to identify an underlying mathematical expression that best describes a relationship. It begins by building a population of naive random formulas to represent a relationship between known independent variables and their dependent variable targets in order to predict new data. Each successive generation of programs is then evolved from the one that came before it by selecting the fittest individuals from the population to undergo genetic operations.
Features
- gplearn implements Genetic Programming in Python
- Documentation available
- gplearn supports regression through the SymbolicRegressor
- gplearn is built on scikit-learn and a fairly recent copy (0.22.1+) is required for installation
- Examples available
- gplearn retains the familiar scikit-learn fit/predict API and works with the existing scikit-learn pipeline and grid search modules
Project Samples
