c45-trees
There are 1 repository under c45-trees topic.
serengil / chefboost
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
Language:Python 443RaczeQ / scikit-learn-C4.5-tree-classifier
A C4.5 tree classifier based on a zhangchiyu10/pyC45 repository, refactored to be compatible with the scikit-learn library.
Language:Python 28fritzwill / decision-tree
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
Language:Python 11fknince / veri-madenciligiPaketi
Bu pakette Veri Madenciliği'nin kendi yazdığım önemli sınıflandırma algoritmalarından C4.5 - ID3 - Linear Regression ve Twoing algoritmaları bulunmaktadır.
Language:Python 8- Language:PHP 8
- Language:Python 7
Sparxxz / Particle-Swarm-Optimisation-C4.5
This is a type of optimisation technique using PSO+C4.5 algorithm, mainly used as Gene Selection Algorithm
Language:Jupyter Notebook 6BigTailFox / C45-Decision-Tree-Python
An implement of the classic machine learning algorithm C4.5 decision tree.
Language:Python 2Igorlinharesb / Classificador-de-Sinais-com-KNN
Trabalho prático da disciplina de Reconhecimento de padrões que consiste na implementação do algoritmo KNN para classificar sinais de áudio e de ECG.
Language:MATLAB 2uab-projects / decision-trees
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).
Language:Python 2Andyccs / C4.5-Decision-Tree-Implementation
C4.5 Decision Tree Implementation
Language:Java 1Davidmenamm / Data_Science_Decision_Trees
Implementing binary classification for id3, c45 and cart trees.
Language:Python 1nikhil-iyer-97 / Decision-Tree-Implementation
Decision tree implementation in C++ to classify and predict salary of people using ID3 and C4.5 algorithms
Language:C++ 1wang-jinghui / Machine-Learning-Algorithm
machine learning algorithm
Language:Python 1ablanco1950 / ABALONE_DECISIONTREE_C4-5
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, 29 for the next node in the hierarchy corresponding to field 7, and 33 in the last node corresponding to field 4. The values ??of each field have been associated with indices, which can encompass several real values. the values ??of these indices are those that have been considered for the calculation of entropies and for making a branching of values ??at each node. A hit rate of around 58% is obtained, that is, in the low range of the existing procedures to treat this multiclass file, which are detailed in the documentation to download from https://archive.ics.uci.edu/ml/ datasets / abalone The depth of the tree has been increased without obtaining significant improvements. Nor has it been significantly improved by applying adaboost. Resources: Spyder 4 On the c: drive there should be the abalone-1.data file downloaded from https://archive.ics.uci.edu/ml/datasets/abalone Functioning: From Spyder run: AbaloneDecisionTree_C4-5-ThreeLevels.py The screen indicates the number of hits and failures and in the file C:\AbaloneCorrected.txt the records of the test file (records 3133 to 4177 of abalone-1.data) with an indication of whether their predicted class values ??coincide with the reals, the predicted class value and the order number of the record in abalone-1.data The following programs are also attached: AbaloneDecisionTree_ID3.py and AbaloneDecisionTree_C4-5_parameters.py that have served to calculate the necessary parameters to build the tree. Cite this software as: ** Alfonso Blanco García ** ABALONE_DECISIONTREE_C4-5 References: https://archive.ics.uci.edu/ml/datasets/abalone
Language:Python 0Using 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%
Language:Python 0- Language:C 0
BedirhanSisman / multiThreadKullanarakKararAgaciOlusturma
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.
Language:C# 0nightspite / id3-c45-classifier
Simple implementation of the ID3 + C4.5 algorithm for decision tree learning
Language:Python 0saitamawashere / Algoritma-C45-Saitamacode
Simple Calculated and Implementation of C4.5 Algorithm With Python
Language:Python 0saliherdemk / C4.5-Algorithm-Visulizer
Visualization of C4.5 Algorithm
Language:JavaScript 0- Language:Java 0
- Language:Jupyter Notebook 0
A 3-level decision tree achieves a 76.48% success rate in the SUSY file test (https://archive.ics.uci.edu/ml/datasets/SUSY)
Language:Pythonalannapaiva / DecisionTree
Implementation of the C4.5 classifier - Decision Tree
Language:Jupyter Notebook- Language:Jupyter Notebook
heriberto2300 / Proyecto-final-Tratamiento
Construcción, evaluación y comparación de un clasificador K-NN y un Árbol de decisión C4.5 para la materia "Tratamiento de la Información"
Language:Javaleofansq / EI229-Image-BinaryClassification
SJTU EI229: Image Classification based on machine learning; 基于CART和C4.5的图像二分类
Language:MATLAB- Language:Java
