Machine Learning: Introduction to XGBoost Algorithm in Detail

Presented by WWCode San Diego Speakers: Dr. Sophia Song Join us for another in-depth machine learning lecture from Dr. Sophia Song! Recently, XGBoost (Extreme Gradient Boosting) is a most commonly used algorithm in applied machine learning and Kaggle competitions because of its higher predicting power and performance. XGBoost is a decision-tree-based ensemble Machine Learning algorithm achieved by improvisation on gradient boosting framework created by Chen & Guestrin (2016). Gradient boosting is a supervised learning algorithm which combines an ensemble of simpler and weaker models to accurately predict a target variable; it supports various objective functions, such as regression, classification and ranking. Speaker: Dr. Sophia Song is a data scientist and data engineer. Her interest lies in integrating data science, data engineering and artificial intelligence to solve various business problems and help the company to improve revenue. Her current work mainly focuses on utilizing recommendation systems for commercial properties. Previously, she has has worked as a data scientist at CoStar, Accenture and CoxAuto. At Accenture, she utilized machine learning algorithms to segment users into different cohorts for assisting recurring payment collection. At CoxAuto, she collaborated with her coworkers to build novel machine learning algorithms to predict car prices. Prior to her industrial tenure, Sophia had more than ten years&experience in academia. She received her Ph.D...