• Mohammed Munavvar Nazeeb

Tree boosting is a highly e ective and widely used machine learning method. In this paper, we describe a scalable end-to-end tree boosting system called XGBoost, which is used widely by data scientists to achieve state-of-the-art results on many machine learning challenges. We propose a novel sparsity-aware algorithm for sparse data and weighted quan-tile sketch for approximate tree learning. More importantly, we provide insights on cache access patterns, data compres-sion and sharding to build a scalable tree boosting system. By combining these insights, XGBoost scales beyond billions of examples using far fewer resources than existing systems.

Large-scale Machine Learning

"XGBoost: Optimization Technique and a Scalable Tree Boosting System for Machine Learning Algorithms", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 3, page no.548-558, March-2019, Available :http://www.jetir.org/papers/JETIR1903082.pdf

"XGBoost: Optimization Technique and a Scalable Tree Boosting System for Machine Learning Algorithms", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 3, page no. pp548-558, March-2019, Available at : http://www.jetir.org/papers/JETIR1903082.pdf