Analysis of Classification Random Forests Model Based on C4.5 Decision Tree Learning Algorithm
Implementation of Random Forest based on C4.5 decision tree algorithm, the adaption of ID3 algorithm. The primary goal of the project is to build an efficient classifier with the notion of ensemble learning method while maintaining randomness. The resultant model performs effectively on disease dataset having discrete attributes.
If you'd like to see the project in detail, please look at the uploaded Report referred from my senior (I think everybody can learn a lot from kind of way of learning).