random-forest
Mini-Project for COMP61011 - University of Manchester
An Implementation and Analysis of a Random Forest as an Ensemble Method for Classification
The project provides an analysis and survey of the Random Forest machine learning algorithm, for classification. RFs are one of the most popular models for resolving machine learning problems. They're relatively new in comparison to other alternatives and as such are a fertile ground for experimentation. By revising and analyzing in depth the algorithms of RF, decision trees and decision stump, this project delivers a throughout overview of different optimization and enhancements of RF.It also serves as a point of comparison within the parameters an behavior inside the very RF and carries out further comparisons with Support Vector Machines.
This repository also contains some of the coursework exercises developed.