Implementing decision trees with the C4.5 algorithm - Part 1
Starting today, we are launching a series of articles on decision trees, taking a progressive approach with a particular focus on the C4.5 algorithm. Our goal is to blend theoretical concepts with practical implementation (in C#), offering clear illustrations of the associated challenges.
Decision trees are a popular machine learning algorithm used for both classification and regression tasks. They are versatile and can be applied to a wide range of problems.
Informally, a decision tree breaks down a dataset into smaller subsets while simultaneously creating a tree-like structure of decisions. Each node in the tree represents a decision based on a particular feature, and the branches represent the possible outcomes of that decision. The leaves of the tree correspond to the final predicted outcomes.
Decision trees are easy to understand and interpret, making them a valuable tool for both beginners and experts in machine learning. They work well for both numerical and categorical data and can handle non-linear relationships between features and the target variable. The C4.5 algorithm is one of the well-known algorithms for constructing decision trees and our aim in this series is to implement it.
- Implementing decision trees with the C4.5 algorithm - Part 2
- Implementing decision trees with the C4.5 algorithm - Part 3
- Implementing decision trees with the C4.5 algorithm - Part 4
- Implementing decision trees with the C4.5 algorithm - Part 5
The following textbooks on this topic merit consultation. These books extend beyond decision trees and covers a myriad of expansive and general machine learning topics.
- Machine Learning: An Algorithmic Perspective (Marsland)
- Probabilistic Machine Learning: An Introduction (Murphy)
- Probabilistic Machine Learning: Advanced Topics (Murphy)
Without further ado and as usual, let's begin with a few prerequisites to correctly understand the underlying concepts. Continue here.