Decision trees

Source: Wikipedia. Pages: 26. Chapters: Alternating decision tree, C4.5 algorithm, CHAID, Decision rules, Decision stump, Decision tree learning, Decision tree model, Gene expression programming, Gradient boosting, Grafting (decision trees), ID3 algorithm, Incremental decision tree, Information gain in decision trees, Information gain ratio, Logistic model tree, Pruning (decision trees), Random forest. Excerpt: Gene expression programming (GEP) is an evolutionary algorithm that creates computer programs or models. These computer programs are complex tree structures that learn and adapt by changing their sizes, shapes, and composition, much like a living organism. And like living organisms, the computer programs of GEP are also encoded in simple linear chromosomes of fixed length. Thus, GEP is a genotype-phenotype system, benefiting from a simple genome to keep and transmit the genetic information and a complex phenotype to explore the environment and adapt to it. GEP has been criticized for not being a major improvement over other genetic programming techniques. In many experiments, it did not perform better than existing methods. Evolutionary algorithms use populations of individuals, select individuals according to fitness, and introduce genetic variation using one or more genetic operators. Their use in artificial computational systems dates back to the 1950s where they were used to solve optimization problems (e.g. Box 1957 and Friedman 1959). But it was with the introduction of evolution strategies by Rechenberg in 1965 that evolutionary algorithms gained popularity. A good overview text on evolutionary algorithms is the book ¿An Introduction to Genetic Algorithms¿ by Mitchell (1996). Gene expression programming belongs to the family of evolutionary algorithms and is closely related to genetic algorithms and genetic programming. From genetic algorithms it inherited the linear chromosomes of fixed length; and from genetic programming it inherited the expressive parse trees of varied sizes and shapes. In gene expression programming the linear chromosomes work as the genotype and the parse trees as the phenotype, creating a genotype/phenotype system. This genotype/phenotype system is multigenic, thus encoding multiple parse trees in each chromosome. This means that the computer programs created by GEP are composed of multiple parse trees. Because these parse trees are th