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 focuses on implementing one of these Algorithms:
The ID3 Algorithm.
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 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 file.
- Authors → Gonçalo Esteves and Nuno Gomes
- Course → Artificial Intelligence [CC2006]
- University → Faculty of Sciences, University of Porto
README.md by Gonçalo Esteves