Member-only storyFarzad MahmoodinobarFollowTowards Data Science--ShareXGBoost has become one of the most popular well-rounded regressors and/or classifiers for all machine learning practitioners. If you ask a data scientist what Model they would use for an unknown task, without any other information, odds are they will choose Xgboost given the vast types of use cases it can be applied to — it is quick, reliable, versatile and very easy to implement.Today, we will conceptually review XGBoost, walk through the implementation of it and compare its performance to several other models for a classification task.Let’s get started!XGBoost stands for Extreme Gradient Boosting. It is a gradient boosting decision tree type of a model, that can be used both for supervised regression and classification tasks. We used a few terms to define XGBoost so let’s walk through them one by one to better understand them. I will provide a brief overview of each concept and will include links to other posts with more details for those interested in a deeper discussion.----Towards Data ScienceApplied Scientist @ Amazon | https://www.linkedin.com/in/fmnobar/Farzad MahmoodinobarinTowards Data Science--1Heiko HotzinTowards Data Science--16Giuseppe ScalamognainTowards Data Science--14Farzad Mahmoodinobar--5Uttam Kumar--Deniz Gunay--Automatain𝐀𝐈 𝐦𝐨𝐧𝐤𝐬.𝐢𝐨--Tasmay Pankaj TibrewalinLow Code for Data Science--4Shawhin TalebiinTowards Data Science--6Paresh Patil--HelpStatusWritersBlogCareersPrivacyTermsAboutText to speechTeams