Extreme Gradient Boosting using Squared Logistics Loss function
Anju
, Akaash Vishal Hazarika
XGBoost, AdaBoost, Random Forest, Big Data, Boosting, Loss, Logistics Loss Function
Tree boosting has empirically proven to be a highly effective approach to predictive modeling. It has shown remarkable results for a vast array of problems. More recently, a tree boosting method known as XGBoost has gained popularity by winning numerous machine learning competitions. In this manuscript, we will investigate how XGBoost differs from the more traditional ensemble techniques. Moreover, we will discuss the regularization techniques that these methods offer and the effect these have on the models. In addition to this, we will attempt to answer the question of why XGBoost seems to win so many competitions. To do this, we will provide some arguments for why tree boosting, and in particular XGBoost, seems to be such a highly effective and versatile approach to predictive modeling. The core argument is that tree boosting can be seen to adaptively determine the local neighborhoods of the model. Tree boosting can thus be seen to take the bias-variance tradeoff into consideration during model fitting. XGBoost further introduces some improvements which allow it to deal with the bias-variance tradeoff even more carefully. We performed these techniques in outliers also. Additionally, we perform XGBoost with a loss function named squared logistics loss (SqLL) and find out loss percentage. Also we applied this SqLL with other algorithm also.
"Extreme Gradient Boosting using Squared Logistics Loss function", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.2, Issue 8, page no.54 - 61, August-2017, Available :https://ijsdr.org/papers/IJSDR1708010.pdf
Volume 2
Issue 8,
August-2017
Pages : 54 - 61
Paper Reg. ID: IJSDR_170694
Published Paper Id: IJSDR1708010
Downloads: 000347030
Research Area: Engineering
Country: mahendergarh, Haryana, India
ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJSDR(IJ Publication) Janvi Wave
