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

Simple implementation of the ID3 + C4.5 algorithm for decision tree learning

Simple Calculated and Implementation of C4.5 Algorithm With Python

Visualization of C4.5 Algorithm

C4.5 PHP Library

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

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%

A 3-level decision tree achieves a 76.48% success rate in the SUSY file test (https://archive.ics.uci.edu/ml/datasets/SUSY)

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, …

Implementing binary classification for id3, c45 and cart trees.

Implementation of the C4.5 classifier - Decision Tree

Trabalho prático da disciplina de Reconhecimento de padrões que consiste na implementação do algoritmo KNN para classificar sinais de áudio e de ECG.

SJTU EI229: Image Classification based on machine learning; 基于CART和C4.5的图像二分类

An implement of the classic machine learning algorithm C4.5 decision tree.

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

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

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