{"id":21525498,"url":"https://github.com/estevesx10/ml1-adaboost-analysis-optimization","last_synced_at":"2025-10-24T01:03:50.605Z","repository":{"id":245918637,"uuid":"781672935","full_name":"EstevesX10/ML1-AdaBoost-Analysis-Optimization","owner":"EstevesX10","description":"AdaBoost Analysis and Optimization [Machine Learning I Course Project]","archived":false,"fork":false,"pushed_at":"2024-12-17T15:46:42.000Z","size":24389,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-01-24T05:27:11.940Z","etag":null,"topics":["adaboostclassifier","boosting-ensemble","weak-learners"],"latest_commit_sha":null,"homepage":"","language":"Jupyter 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Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"\u003cp\u003e\n\u003cdiv align=\"center\"\u003e\n\n# ML1 | AdaBoost Classifier Analysis \u0026 Optimization\n\u003c/div\u003e\n\u003c/p\u003e\n\n\u003cp align=\"center\" width=\"100%\"\u003e\n    \u003cimg src=\"./AdaBoost/Assets/Boosting.gif\" width=\"40%\" /\u003e\n\u003c/p\u003e\n\n\u003cdiv align=\"center\"\u003e\n    \u003ca\u003e\n        \u003cimg src=\"https://img.shields.io/badge/Made%20with-Jupyter-white?style=for-the-badge\u0026logo=Jupyter\u0026logoColor=white\"\u003e\n    \u003c/a\u003e\n\u003c/div\u003e\n\n\u003cbr/\u003e\n\n\u003cdiv align=\"center\"\u003e\n    \u003ca href=\"https://github.com/EstevesX10/ML1-AdaBoost-Analysis-Optimization/blob/main/LICENSE\"\u003e\n        \u003cimg src=\"https://img.shields.io/github/license/EstevesX10/ML1-AdaBoost-Analysis-Optimization?style=flat\u0026logo=gitbook\u0026logoColor=white\u0026label=License\u0026color=white\"\u003e\n    \u003c/a\u003e\n    \u003ca href=\"\"\u003e\n        \u003cimg src=\"https://img.shields.io/github/repo-size/EstevesX10/ML1-AdaBoost-Analysis-Optimization?style=flat\u0026logo=googlecloudstorage\u0026logoColor=white\u0026logoSize=auto\u0026label=Repository%20Size\u0026color=white\"\u003e\n    \u003c/a\u003e\n    \u003ca href=\"\"\u003e\n        \u003cimg src=\"https://img.shields.io/github/stars/EstevesX10/ML1-AdaBoost-Analysis-Optimization?style=flat\u0026logo=adafruit\u0026logoColor=white\u0026logoSize=auto\u0026label=Stars\u0026color=white\"\u003e\n    \u003c/a\u003e\n    \u003ca href=\"https://github.com/EstevesX10/ML1-AdaBoost-Analysis-Optimization/blob/main/DEPENDENCIES.md\"\u003e\n        \u003cimg src=\"https://img.shields.io/badge/Dependencies-DEPENDENCIES.md-white?style=flat\u0026logo=anaconda\u0026logoColor=white\u0026logoSize=auto\u0026color=white\"\u003e \n    \u003c/a\u003e\n\u003c/div\u003e\n\n## Project Overview\n\nIn recent years, the rise of **Artificial Intelligence** and the widespread use of **Machine Learning** have revolutionized the way we tackle complex real-world challenges. However, due to the **diverse nature of data involved**, choosing the right algorithm is crucial to achieve efficient and effective solutions. Therefore, understanding the **strengths** and **weaknesses** behind different Machine Learning algorithms, and knowing how to **adapt them** to meet specific challenges, can become a fulcral skill to develop.\n\nFurthermore, since the **choice of algorithm** greatly depends on the specific task and data involved, it's clear that there is no **\"Master Algorithm\"** (No algorithm can solve every problem). For example, while Linear Discriminants effectively delineate boundaries in data that is linearly separable, they struggle to capture relationships in more complex, higher-dimensional spaces.\n\nThis Project focuses on the following topic:\n\n\u003cdiv align=\"center\"\u003e\n\n\u003e With no Master Algorithm, is it possible to improve a existing Machine Learning Algorithm in characteristics it struggles the most?\n\u003c/div\u003e\n\nTherefore, after choosing a **Machine Learning Algorithm** and gaining a thorough understanding of its theoretical and empirical aspects, we aim to **refine it**, specifically **targeting its weaknesses** in solving classification problems.\n\n\u003cp align=\"center\" width=\"100%\"\u003e\n    \u003cimg src=\"./AdaBoost/Assets/ThoughtProcess.png\" width=\"45%\" /\u003e\n\u003c/p\u003e\n\n## Classifier Selection\n\nNowadays, since **singular Machine Learning Algorithms** can fall short to predict the whole data given, we decided to study an **Ensemble Algorithm**. Since these Algorithms can combine outputs of multiple models it makes them more prone to **better address more complex problems** and **provide better solutions**.\n\nConsequently, after careful consideration, we decided to focus on enhancing the **AdaBoost Algorithm M1**, which is employed in **binary classification problems**.\n\n\u003ctable width=\"100%\"\u003e\n  \u003ctr\u003e\n    \u003ctd width=\"45%\"\u003e\n        \u003cdiv align=\"center\"\u003e\n        \u003cb\u003eAdaBoost\u003c/b\u003e (Adaptive Boosting) is a type of ensemble learning technique used in machine learning to solve both \u003cb\u003eclassification\u003c/b\u003e and \u003cb\u003eregression\u003c/b\u003e problems. It consists on training a \u003cb\u003eseries of weak classifiers\u003c/b\u003e on the dataset. Therefore, with each iteration, the algorithm \u003cb\u003eincreases the focus\u003c/b\u003e on data points that were \u003cb\u003epreviously predicted incorrectly\u003c/b\u003e.\n        \u003c/div\u003e\n    \u003c/td\u003e\n    \u003ctd width=\"55%\"\u003e\n        \u003cp align=\"center\"\u003e\u003cimg src=\"./AdaBoost/Assets/AdaBoost_Overview.jpeg\" width=\"100%\" height=\"auto\"/\u003e\n        \u003c/p\u003e\n    \u003c/td\u003e\n  \u003c/tr\u003e\n\u003c/table\u003e\n\nAs a result, the AdaBoost algorithm builds a model by considering all the individual **weak classifiers** which are **weighted based on their performance**. Consequently, classifiers with **higher predictive accuracy contribute more to the final decision** which **reduces the influence of less accurate ones** in the final prediction. \n\n## Authorship\n\n- **Authors** \u0026#8594; [Gonçalo Esteves](https://github.com/EstevesX10) and [Nuno Gomes](https://github.com/NightF0x26)\n- **Course** \u0026#8594; Machine Learning I [CC2008]\n- **University** \u0026#8594; Faculty of Sciences, University of Porto\n\n\u003cdiv align=\"right\"\u003e\n\u003csub\u003e\n\u003c!-- \u003csup\u003e\u003c/sup\u003e --\u003e\n\n`README.md by Gonçalo Esteves`\n\u003c/sub\u003e\n\u003c/div\u003e\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Festevesx10%2Fml1-adaboost-analysis-optimization","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Festevesx10%2Fml1-adaboost-analysis-optimization","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Festevesx10%2Fml1-adaboost-analysis-optimization/lists"}