A Lightweight Decision Tree Framework supporting regular algorithms: ID3, C4.5, CART, CHAID and Regression Trees; some advanced techniques: Gradient Boosting, Random Forest and Adaboost w/categorical features support for Python

Python 3 implementation of decision trees using the ID3 and C4.5 algorithms. ID3 uses Information Gain as the splitting criteria and C4.5 uses Gain Ratio

A C4.5 tree classifier based on a zhangchiyu10/pyC45 repository, refactored to be compatible with the scikit-learn library.

C4.5 PHP Library

Bu pakette Veri Madenciliği'nin kendi yazdığım önemli sınıflandırma algoritmalarından C4.5 - ID3 - Linear Regression ve Twoing algoritmaları bulunmaktadır.

Decision tree implementation in C++ to classify and predict salary of people using ID3 and C4.5 algorithms

In this project we'll try to implement and learn about decision trees the in artificial intelligence subject KRU (Knowledge, reasoning and uncertainty or in Catalan, a region from Spain we are living: Coneixement, raonament i incertesa).

This is a type of optimisation technique using PSO+C4.5 algorithm, mainly used as Gene Selection Algorithm

Using the decision tree technique based on entropy calculation, this application calculates the hit rate of the HASTIE file with a hit rate higher than 99%

machine learning algorithm

Construcción, evaluación y comparación de un clasificador K-NN y un Árbol de decisión C4.5 para la materia "Tratamiento de la Información"

Visualization of C4.5 Algorithm

Simple Calculated and Implementation of C4.5 Algorithm With Python

决策树算法c4.5进行影像分类

Implementation of the C4.5 classifier - Decision Tree

ABALONE_DECISIONTREE_C4-5: A procedure is attached that uses the Abalone file (https://archive.ics.uci.edu/ml/datasets/abalone) as test and training . After evaluating the entropy of each field, a tree has been built with the nodes corresponding to fields 0, 7 and 4 and branch values ??in each node: 1 for the root node corresponding to field 0, …

Bu projede bizden istenen multi thread yapısı kullanılarak verilen veri seti üzerinden karar ağacı oluşturulması istenmektedir. Karar ağacı oluşturma aşamasında C4.5 algoritmasının kullanılması istenmektedir. Projenin asıl amacı Thread yapısının kullanılması ve anlaşılmasıdır. Böylece eş zamanlı işlem yapılabilmektedir.