Topic: c45-trees Goto Github
Some thing interesting about c45-trees
Some thing interesting about c45-trees
c45-trees,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
User: ablanco1950
c45-trees,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%
User: ablanco1950
c45-trees,A 3-level decision tree achieves a 76.48% success rate in the SUSY file test (https://archive.ics.uci.edu/ml/datasets/SUSY)
User: ablanco1950
c45-trees,Implementation of the C4.5 classifier - Decision Tree
User: alannapaiva
c45-trees,
User: apucontilde-zz
c45-trees,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.
User: bedirhansisman
c45-trees,An implement of the classic machine learning algorithm C4.5 decision tree.
User: bigtailfox
c45-trees,Implementing binary classification for id3, c45 and cart trees.
User: davidmenamm
c45-trees,Bu pakette Veri Madenciliği'nin kendi yazdığım önemli sınıflandırma algoritmalarından C4.5 - ID3 - Linear Regression ve Twoing algoritmaları bulunmaktadır.
User: fknince
c45-trees,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
User: fritzwill
c45-trees,Construcción, evaluación y comparación de un clasificador K-NN y un Árbol de decisión C4.5 para la materia "Tratamiento de la Información"
User: heriberto2300
c45-trees,Trabalho prático da disciplina de Reconhecimento de padrões que consiste na implementação do algoritmo KNN para classificar sinais de áudio e de ECG.
User: igorlinharesb
c45-trees,SJTU EI229: Image Classification based on machine learning; 基于CART和C4.5的图像二分类
User: leofansq
c45-trees,C4.5 PHP Library
Organization: medansoftware
c45-trees,Simple implementation of the ID3 + C4.5 algorithm for decision tree learning
User: nightspite
c45-trees,Decision tree implementation in C++ to classify and predict salary of people using ID3 and C4.5 algorithms
User: nikhil-iyer-97
c45-trees,A C4.5 tree classifier based on a zhangchiyu10/pyC45 repository, refactored to be compatible with the scikit-learn library.
User: raczeq
c45-trees,Simple Calculated and Implementation of C4.5 Algorithm With Python
User: saitamawashere
c45-trees,Visualization of C4.5 Algorithm
User: saliherdemk
Home Page: https://saliherdemk.github.io/C4.5-Algorithm-Visulizer/
c45-trees,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
User: serengil
Home Page: https://www.youtube.com/watch?v=Z93qE5eb6eg&list=PLsS_1RYmYQQHp_xZObt76dpacY543GrJD&index=3
c45-trees,决策树算法c4.5进行影像分类
User: silenceu
c45-trees,This is a type of optimisation technique using PSO+C4.5 algorithm, mainly used as Gene Selection Algorithm
User: sparxxz
c45-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).
Organization: uab-projects
c45-trees,machine learning algorithm
User: wang-jinghui
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.