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