Alternative Methods via Random Forest to Identify.
The experiments were conducted on five datasets to minimize the mean square error (MSE) of the Random Forest and imputation errors of the MissForest. The results showed the superiority of the proposed method in comparison to the classical Random Forest methods. Optimization of Random Forest Based Methods Applying the Genetic Algorithms.
There is a PhD thesis from one of the Kaggle guys about Understanding Random Forests. And that's actually the title of his thesis. This is the link and i think its a pretty new PhD.
Craig Wright was awarded his PhD by Charles Sturt University in 2017. His doctoral thesis (archived link) is titled The Quantification of Information Systems Risk: A Look at Quantitative Responses.
A COMPARATIVE ANALYSIS OF RANDOM FOREST AND LOGISTIC REGRESSION FOR WEED RISK ASSESSMENT by Chinchu Harris Thesis submitted to the Faculty of the Graduate School of the University of Maryland, College Park, in partial fulfillment of the requirements for the degree of Master of Science 2018 Advisory Committee.
PhD thesis Learning with random forests, defended on Monday, 30th November, 2015. Teaching You can find the slides of the Deep Learning course below.
Random Forest (RF) is one of the most widely used supervised learning methods available. An RF is ensemble of decision tree classi ers with injection of several sources of randomness. It demonstrates a set of improvement over single decision and regression trees and is comparable.
Before buying a product, people usually go to various shops in the market, query about the product, cost, and warranty, and then finally buy the product based on the opinions they received on cost and quality of service. This process is time consuming and the chances of being cheated by the seller are more as there is nobody to guide as to where the buyer can get authentic product and with.