This repository hosts my Python implementation of a decision tree classifier built from scratch, without relying on existing machine learning libraries like scikit-learn. Despite being developed independently, my implementation achieves the exact same accuracy as the decision tree classifier provided by scikit-learn. Features
-
From Scratch Implementation: I've crafted this decision tree classifier entirely from the ground up, providing insights into the inner workings of decision tree algorithms.
-
Comparable Accuracy: I rigorously tested my implementation against scikit-learn's decision tree classifier to ensure identical accuracy in classification tasks.
-
Customizable and Extendable: Easily customize and extend the decision tree algorithm to suit your specific needs. Whether it's modifying splitting criteria, handling missing values, or integrating with other algorithms, my codebase offers flexibility.
-
Educational Resource: Ideal for learning about decision tree algorithms, this repository serves as an educational resource for understanding the fundamentals of machine learning.