Tree Boosting Data Competitions with XGBoost
Tutor / directorDelicado Useros, Pedro Francisco
Document typeMaster thesis
Rights accessOpen Access
Except where otherwise noted, content on this work is licensed under a Creative Commons license : Attribution-NonCommercial-NoDerivs 3.0 Spain
This Master's Degree Thesis objective is to provide understanding on how to approach a supervised learning predictive problem and illustrate it using a statistical/machine learning algorithm, Tree Boosting. A review of tree methodology is introduced in order to understand its evolution, since Classification and Regression Trees, followed by Bagging, Random Forest and, nowadays, Tree Boosting. The methodology is explained following the XGBoost implementation, which achieved state-of-the-art results in several data competitions. A framework for applied predictive modelling is explained with its proper concepts: objective function, regularization term, overfitting, hyperparameter tuning, k-fold cross validation and feature engineering. All these concepts are illustrated with a real dataset of videogame churn; used in a datathon competition.
DegreeMÀSTER UNIVERSITARI EN ESTADÍSTICA I INVESTIGACIÓ OPERATIVA (Pla 2013)