{"id":17160681,"url":"https://github.com/michaeldorner/decisiontrees","last_synced_at":"2025-04-10T17:37:44.910Z","repository":{"id":15526951,"uuid":"18261467","full_name":"michaeldorner/DecisionTrees","owner":"michaeldorner","description":"Seminar work \"Decision Trees - An Introduction\" with presentation, seminar paper, and Python implementation","archived":false,"fork":false,"pushed_at":"2016-11-23T12:18:00.000Z","size":25897,"stargazers_count":129,"open_issues_count":0,"forks_count":56,"subscribers_count":3,"default_branch":"master","last_synced_at":"2025-03-24T15:13:08.247Z","etag":null,"topics":["decision-trees","paper","tex","theory"],"latest_commit_sha":null,"homepage":null,"language":"TeX","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"gpl-3.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/michaeldorner.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2014-03-30T09:55:09.000Z","updated_at":"2025-03-19T12:49:50.000Z","dependencies_parsed_at":"2022-08-04T05:00:21.588Z","dependency_job_id":null,"html_url":"https://github.com/michaeldorner/DecisionTrees","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/michaeldorner%2FDecisionTrees","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/michaeldorner%2FDecisionTrees/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/michaeldorner%2FDecisionTrees/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/michaeldorner%2FDecisionTrees/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/michaeldorner","download_url":"https://codeload.github.com/michaeldorner/DecisionTrees/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248262046,"owners_count":21074237,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["decision-trees","paper","tex","theory"],"created_at":"2024-10-14T22:25:38.673Z","updated_at":"2025-04-10T17:37:44.894Z","avatar_url":"https://github.com/michaeldorner.png","language":"TeX","funding_links":[],"categories":[],"sub_categories":[],"readme":"Decision Trees - An Introduction\n=============\n\u003cp align=\"center\" \u003e\u003cimg src=\"https://raw.githubusercontent.com/michaeldorner/DecisionTrees/master/01_Seminar%20Paper/content/decisiontree.png\" width=\"200px\" alt=\"decisiontree\" title=\"decisiontree\"\u003e\u003c/p\u003e\n\n\nAbstract\n--------------------\nThis project work emerges in the context of the course *Artificial Intelligence* in the winter semester 2013/2014 at [Friedrich-Alexander-University, Erlangen](http://www.fau.eu). Beside this seminar paper, an introductory presentation was conducted and an implementation for decision tree was developed. The presentation is available only in German.\n\nIn the scope of this seminar paper, a small introduction to theory and application of decision trees shall be given.\n\nAfter this short introduction a theoretical consideration shall guide to a practical part, which shall clarify the theoretical part by examples. The last part shall summarize and compare the introduced algorithm and shall give a small outlook to not tackled research fields of decision trees.\n\nOn the contrary to the presentation during the seminar, this seminar paper expects a basic knowledge about graph theory, complexity, and machine learning. Instead of an introduction to these underlaying topics, a deeper look inside four decision tree algorithm families shall be given: CHAID, CART, ID3, and C4.5.\n\nThe focus of all Python implementation is on classification. This limitation is not owed to the insufficient importance of regression calculating, but a wider look would push boundaries of this seminar paper.\n\n\nTable of Content\n--------------------\n* Introduction\n  - What is a decision tree?\n  - Taxonomy\n  - About this paper\n* Theory of Decision Trees\n  - Definitions\n  - Decision Tree Learning\n      - Splitting Criterion\n      - Stopping Criterion\n      - Tree Pruning\n  - Selected Algorithms\n      - Chi-squared Automatic Interaction Detector (CHAID) \n      - IterativeDichotomiser 3 (ID3)\n      - Classification And Regression Tree (CART) \n      - C4.5\n  - Discussion\n      - Advantages\n      - Disadvantages\n  - Outlook\n      - Complexity\n      - Missing Attributes\n      - Random Forests\n* Summary \u0026 Conclusion\n  - Applications\n  - Programming Example\n  - Summary\n\n\nQuicklinks\n--------------------\n* [Seminar Paper .pdf](https://raw.githubusercontent.com/michaeldorner/DecisionTrees/master/01_Seminar%20Paper/seminarpaper.pdf)\n* [Presentation .pdf (German)](https://raw.githubusercontent.com/michaeldorner/DecisionTrees/master/02_Praesentation/praesentation.pdf)\n* [Python source code](https://github.com/michaeldorner/DecisionTrees/tree/master/03_Python%20Code)\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmichaeldorner%2Fdecisiontrees","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmichaeldorner%2Fdecisiontrees","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmichaeldorner%2Fdecisiontrees/lists"}