https://github.com/estevesx10/ai-decision-trees-id3

Decision Trees - ID3 [Artificial Intelligence Course Project]
https://github.com/estevesx10/ai-decision-trees-id3

decision-trees id3-decision-tree supervised-machine-learning

Last synced: 7 months ago
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Decision Trees - ID3 [Artificial Intelligence Course Project]

• Host: GitHub
• URL: https://github.com/estevesx10/ai-decision-trees-id3
• Owner: EstevesX10
• License: mit
• Created: 2024-03-26T22:49:53.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2024-06-19T00:19:44.000Z (over 1 year ago)
• Last Synced: 2025-01-24T05:27:36.961Z (8 months ago)
• Topics: decision-trees, id3-decision-tree, supervised-machine-learning
• Language: Jupyter Notebook
• Homepage:
• Size: 13.2 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          


# AI | Decision Trees [ID3 with Python]





    





    

        

    








    

        

    

    

        

    

    

        

    

    

         

    



## Project Overview

Nowadays, **Decision Trees** represent one of the most popular **Supervised Machine Learning Algorithms**. They are commonly used in **classification problems**, but yet versatile enough to address **regression tasks** as well. The core concept behind a decision tree resides in **consecutive partitions of data** using feature-based decision-making processes that can be visually represented with a tree structure. This structure consists of nodes and leaves where each internal node denotes a test on an attribute, each branch represents an outcome of the test, and each leaf node holds a **target class label**.

These can vary from:

- **ID3 Algortihm**.

- **CART Algorithm**.

- **(C4.5) Algorithm**.

And even used within **ensemble models**:

- **Random Forest** (Use of Multiple Decision Trees to make a decision).

- **AdaBoost** (Use of Stumps as Weak Learners to make a decision).

- **XGBoost** (One of the best decision trees algorithms used in real world applications).

Consequently, due to their usefullness, this [**Assignment #2**](https://github.com/EstevesX10/AI-Decision-Trees-ID3/blob/main/Decision%20Trees%20-%20ID3/Decision_Trees_ID3.ipynb) focuses on implementing one of these Algorithms: 



> The ID3 Algorithm.



## Project Development (Dependencies & Execution)

As a request from our professors this project was developed using a `Notebook`. Therefore if you're looking forward to test it out yourself, keep in mind to either use a **[Anaconda Distribution](https://www.anaconda.com/)** or a 3rd party software that helps you inspect and execute it. 

Therefore, for more informations regarding the **Virtual Environment** used in Anaconda, consider checking the [DEPENDENCIES.md](https://github.com/EstevesX10/AI-Decision-Trees-ID3/blob/main/DEPENDENCIES.md) file.

## Authorship

- **Authors** → [Gonçalo Esteves](https://github.com/EstevesX10) and [Nuno Gomes](https://github.com/NightF0x26)

- **Course** → Artificial Intelligence [CC2006]

- **University** → Faculty of Sciences, University of Porto

 





`README.md by Gonçalo Esteves`