A comparison between Neural networks, Lasso regularized Logistic regression, and Gradient boosted trees in modeling binary sales
Rickard Strandberg, Johan Låås · KTH Publication Database DiVA (KTH Royal Institute of Technology) · 2019
The primary purpose of this thesis is to predict whether or not a customer will make a purchase from a specific item category. The historical data is provided by the Nordic online-based IT-retailer Dustin. The secondary purpose is to evaluate how well a fully connected feed forward neural network performs as compared to Lasso regularized logistic regression and gradient boosted trees (XGBoost) on this task. This thesis finds XGBoost to be superior to the two other methods in terms of prediction accuracy, as well as speed.