> For the complete documentation index, see [llms.txt](https://www.mlcompendium.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://www.mlcompendium.com/predictive-ml/learning-classifier-systems.md).
# Learning Classifier Systems
Learning Classifier Systems (LCS) sit next to genetic algorithms for the case where the learner is a rule population rather than a single model. The page first defines LCS, then narrows to XCS, the variant with a scikit-learn implementation and a tutorial.
The same notes are in [Genetic Algorithms & Genetic Programming](/predictive-ml/genetic-algorithms-and-genetic-programming.md).
### LCS
The starting point is the Wikipedia definition, because it names the two parts every LCS has. (WIKI) [Learning classifier systems](https://en.wikipedia.org/wiki/Learning_classifier_system), or LCS, are a paradigm of [rule-based machine learning](https://en.wikipedia.org/wiki/Rule-based_machine_learning) methods that combine a discovery component (e.g. typically a [genetic algorithm](https://en.wikipedia.org/wiki/Genetic_algorithm)) with a learning component (performing either [supervised learning](https://en.wikipedia.org/wiki/Supervised_learning), [reinforcement learning](https://en.wikipedia.org/wiki/Reinforcement_learning), or [unsupervised learning](https://en.wikipedia.org/wiki/Unsupervised_learning)). The learning component is the choice that matters: in supervised learning the training data is labeled with the expected answers, while in unsupervised learning the model finds patterns or structures in unlabeled data.
### XCS
With the general definition in place, XCS is the concrete variant that picks reinforcement learning as its learning component. [XCS](http://hosford42.github.io/xcs/) is a type of [Learning Classifier System (LCS)](http://en.wikipedia.org/wiki/Learning_classifier_system), a [machine learning](http://en.wikipedia.org/wiki/Machine_learning) algorithm that utilizes a [genetic algorithm](http://en.wikipedia.org/wiki/Genetic_algorithm) acting on a rule-based system, to solve a [reinforcement learning](http://en.wikipedia.org/wiki/Reinforcement_learning) problem.
To run it, [Scikit-xcs](https://github.com/UrbsLab/scikit-XCS) is the scikit-learn compatible implementation of XCS, described by its authors as the most popular and best studied learning classifier system algorithm to date. The figure below is from that repository.
Scikit-xcs.
For a guided walk through the same algorithm, the XCS [Tutorial](https://pythonhosted.org/xcs/) goes with the hosford42 XCS package linked above.
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