c++ incremental decision tree

DecisionTreeC45 is a library for creating decision trees using the C4.5 algorithm.

Repository tugas UAS mata kuliah Kecerdasan Buatan. Tugas yang dikerjakan yaitu membuat sistem pendukung keputusan untuk penentuan ciri ubi jalar dengan metode decision tree algoritma C4.5.

Sistem Pendukung Keputusan untuk menentukan layak dan tidaknya seseorang mendapatkan bantuan PKH dengan menggunakan machine learning yaitu C4.5 dan K-Means

Data Mining project to implement Quinlan's C4.5 decision tree algorithm from scratch for medical data mining using the Thyroid allbp dataset

Decision tree regression implementation by MATLAB.

BBM465*ASG4 - In this experiment, we did visual analysis using image files consisting of screenshots. With the threat intelligence module we produced for anti-phishing, we completed website brand classification with screenshots of phishing websites.

C4.5 atm data mining

KLASIFIKASI DATA PENYAKIT DIABETES MENGGUNAKAN ALGORITMA DECISION TREE DAN PARTICLE SWARM OPTIMIZATION

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

Visualization of C4.5 Algorithm

Loan Approval Predictor using python

myID3 and myC45 modules implementation (Tubes1B), myMLP module implementation with mini-batch gradient descent (Tubes1C) and 10-fold cross validation scheme implementation (Tubes1D)

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%

Membuat klasifikasi penyakit daun teh menggunakan algoritma C45/Decision Tree

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