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XGBoost -- The Undisputed GOAT!

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In this article, we'll learn about XGBoost, its background, its widely accepted usage in competitions such as Kaggle's and help you build an intuitive understanding of it by diving into the foundation of this algorithm. XGBoost is an algorithm that is highly flexible, portable, and efficient which is based on a decision tree for ensemble learning for Machine Learning that uses the distributed gradient boosting framework. Machine Learning algorithms are implemented with XGBoost under the Gradient boosting framework. XGBoost is capable of solving data science problems accurately in a short duration with its parallel tree boosting which is also called Gradient Boosting Machine (GBM), Gradient Boosting Decision Trees (GBDT). It is extremely portable and cross-platform enabled such that the very same code can be run on the different major distributed environments such as Hadoop, MPI, and SGE and enables solving problems with well over billions of examples.


XGBoost: Enhancement Over Gradient Boosting Machines

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XGBoost was originally developed by Tianqi Chen in his paper titeled "XGBoost: A Scalable Tree Boosting System." XGBoost itself is an enhancement to the gradient boosting algorithm created by Jerome H. Friedman in his paper titled "Greedy Function Approximation: A Gradient Boosting Machine." Both papers are well worth exploring.


Gentle Introduction of XGBoost Library – Mohit Sharma – Medium

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In this article, you will discover XGBoost and get a gentle introduction to what it is, where it came from and how you can learn more. Bagging: It is an approach where you take random samples of data, build learning algorithms and take simple means to find bagging probabilities. Boosting: Boosting is similar, however, the selection of sample is made more intelligently. We subsequently give more and more weight to hard to classify observations. XGBoost is an optimized distributed gradient boosting library designed to be highly efficient, flexible and portable.


A Complete Tutorial on Tree Based Modeling from Scratch (in R & Python)

#artificialintelligence

Tree based learning algorithms are considered to be one of the best and mostly used supervised learning methods. Tree based methods empower predictive models with high accuracy, stability and ease of interpretation. Unlike linear models, they map non-linear relationships quite well. They are adaptable at solving any kind of problem at hand (classification or regression). Methods like decision trees, random forest, gradient boosting are being popularly used in all kinds of data science problems. Hence, for every analyst (fresher also), it's important to learn these algorithms and use them for modeling. This tutorial is meant to help beginners learn tree based modeling from scratch. After the successful completion of this tutorial, one is expected to become proficient at using tree based algorithms and build predictive models. Note: This tutorial requires no prior knowledge of machine learning.


A Complete Tutorial on Tree Based Modeling from Scratch (in R & Python)

#artificialintelligence

Tree based learning algorithms are considered to be one of the best and mostly used supervised learning methods. Tree based methods empower predictive models with high accuracy, stability and ease of interpretation. Unlike linear models, they map non-linear relationships quite well. They are adaptable at solving any kind of problem at hand (classification or regression). Methods like decision trees, random forest, gradient boosting are being popularly used in all kinds of data science problems. Hence, for every analyst (fresher also), it's important to learn these algorithms and use them for modeling. This tutorial is meant to help beginners learn tree based modeling from scratch. After the successful completion of this tutorial, one is expected to become proficient at using tree based algorithms and build predictive models. Note: This tutorial requires no prior knowledge of machine learning.