{"id":6848,"date":"2018-02-01T05:10:16","date_gmt":"2018-01-31T23:40:16","guid":{"rendered":"https:\/\/data-flair.training\/blogs\/?p=6848"},"modified":"2025-07-28T15:34:02","modified_gmt":"2025-07-28T10:04:02","slug":"xgboost-tutorial","status":"publish","type":"post","link":"https:\/\/data-flair.training\/blogs\/xgboost-tutorial\/","title":{"rendered":"XGBoost Tutorial &#8211; What is XGBoost in Machine Learning?"},"content":{"rendered":"<div class='__iawmlf-post-loop-links' style='display:none;' data-iawmlf-post-links='[{&quot;id&quot;:1418,&quot;href&quot;:&quot;https:\\\/\\\/en.wikipedia.org\\\/wiki\\\/Machine_learning&quot;,&quot;archived_href&quot;:&quot;http:\\\/\\\/web-wp.archive.org\\\/web\\\/20251130072921\\\/https:\\\/\\\/en.wikipedia.org\\\/wiki\\\/Machine_learning&quot;,&quot;redirect_href&quot;:&quot;&quot;,&quot;checks&quot;:[{&quot;date&quot;:&quot;2025-12-09 06:41:40&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2025-12-12 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02:25:23&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-04-17 05:49:08&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-04-20 06:38:49&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-04-23 08:15:10&quot;,&quot;http_code&quot;:404},{&quot;date&quot;:&quot;2026-04-26 10:02:48&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-04-29 14:13:55&quot;,&quot;http_code&quot;:429},{&quot;date&quot;:&quot;2026-05-02 19:39:01&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-05-06 04:50:24&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-05-09 06:14:41&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-05-12 08:20:37&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-05-15 09:29:22&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-05-18 11:00:28&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-05-21 13:05:16&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-05-24 13:13:21&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-05-27 13:51:19&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-05-30 15:11:03&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-06-02 18:48:44&quot;,&quot;http_code&quot;:404}],&quot;broken&quot;:false,&quot;last_checked&quot;:{&quot;date&quot;:&quot;2026-06-02 18:48:44&quot;,&quot;http_code&quot;:404},&quot;process&quot;:&quot;done&quot;}]'><\/div>\n<div>\n<div class=\"\">\n<p>In this XGBoost Tutorial, we will study What is XGBoosting. Also, will learn the features of XGBoosting and why we need\u00a0XGBoost Algorithm. We will try to cover all basic concepts like why we use XGBoost, why XGBoosting is good and much more.<\/p>\n<p>So, let&#8217;s start XGBoost Tutorial.<\/p>\n<h3>What is XGBoost?<\/h3>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><em>XGBoost is an algorithm<\/em>. That has recently been dominating applied <a href=\"https:\/\/data-flair.training\/blogs\/machine-learning-tutorial\/\"><strong>machine learning<\/strong><\/a>.<\/div>\n<\/div>\n<div class=\"\"><\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">XGBoost Algorithm is an implementation of<a href=\"https:\/\/data-flair.training\/blogs\/gradient-boosting-algorithm\/\"><strong>gradient boosted<\/strong><\/a> decision trees. That <span class=\"passivevoice\">was designed<\/span> for speed and performance.<\/div>\n<\/div>\n<div class=\"\"><\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><span class=\"adverb\">Basically<\/span>,\u00a0XGBoosting is a type of software library. That you can download and install on your machine. Then have to access it from a variety of interfaces.<\/div>\n<\/div>\n<div class=\"\"><\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><em><span class=\"adverb\">Specifically<\/span>, XGBoosting supports the following main interfaces:<\/em><\/div>\n<\/div>\n<div class=\"\">\n<ul>\n<li class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">Command Line Interface (CLI).<\/li>\n<\/ul>\n<\/div>\n<div class=\"\">\n<ul>\n<li class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">C++ (the language in which the library <span class=\"passivevoice\">is written<\/span>).<\/li>\n<\/ul>\n<\/div>\n<div class=\"\">\n<ul>\n<li class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><a href=\"https:\/\/data-flair.training\/blogs\/python-tutorial-for-beginners\/\"><strong>Python<\/strong><\/a> interface as well as a model in scikit-learn.<\/li>\n<\/ul>\n<\/div>\n<div class=\"\">\n<ul>\n<li class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><a href=\"https:\/\/data-flair.training\/blogs\/r-programming-tutorial\/\"><strong>R<\/strong><\/a> interface as well as a model in the caret package.<\/li>\n<\/ul>\n<\/div>\n<div class=\"\">\n<ul>\n<li class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">Julia.<\/li>\n<\/ul>\n<\/div>\n<div class=\"\">\n<ul>\n<li class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><a href=\"https:\/\/data-flair.training\/blogs\/java-tutorial\/\"><strong>Java<\/strong><\/a>and JVM languages like<a href=\"https:\/\/data-flair.training\/blogs\/scala-control-structures-comprehensive-guide\/\"><strong>Scala<\/strong><\/a> and platforms like <a href=\"https:\/\/data-flair.training\/blogs\/hadoop-tutorial-for-beginners\/\"><strong>Hadoop<\/strong><\/a>.<\/li>\n<\/ul>\n<\/div>\n<div class=\"\">\n<p class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><strong><a href=\"https:\/\/data-flair.training\/blogs\/xgboost-algorithm\/\">Follow this link to know more about XGBoost Algorithms<\/a><\/strong><\/p>\n<h3 class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">XGBoost Features<\/h3>\n<p>Here, in this part of XGBoost Tutorial, we will study features of\u00a0XGBoost.<\/p>\n<div id=\"attachment_9003\" style=\"width: 1210px\" class=\"wp-caption aligncenter\"><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/02\/XGBoost-Features-01-1.jpg\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-9003\" class=\"wp-image-9003 size-full\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/02\/XGBoost-Features-01-1.jpg\" alt=\"XGBoost Tutorial - XGBoost Features-\" width=\"1200\" height=\"628\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/02\/XGBoost-Features-01-1.jpg 1200w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/02\/XGBoost-Features-01-1-150x79.jpg 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/02\/XGBoost-Features-01-1-300x157.jpg 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/02\/XGBoost-Features-01-1-768x402.jpg 768w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/02\/XGBoost-Features-01-1-1024x536.jpg 1024w\" sizes=\"auto, (max-width: 1200px) 100vw, 1200px\" \/><\/a><p id=\"caption-attachment-9003\" class=\"wp-caption-text\">XGBoost Tutorial &#8211; Features of XGBoosting<\/p><\/div>\n<\/div>\n<div class=\"\">\n<h4 class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">a. Model Features<\/h4>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">XGBoost model implementation supports the features of the scikit-learn and <strong>R<\/strong> implementations. Three main forms of gradient boosting <span class=\"passivevoice\">are supported<\/span>:<\/div>\n<\/div>\n<div class=\"\"><\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><strong>Gradient Boosting<\/strong><\/div>\n<\/div>\n<div class=\"\"><\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">This is also called as gradient boosting machine including the learning rate.<\/div>\n<\/div>\n<div class=\"\"><\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><strong>Stochastic Gradient Boosting<\/strong><\/div>\n<\/div>\n<div class=\"\"><\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">This is the boosting with sub-sampling at the row, column, and column per split levels.<\/div>\n<\/div>\n<div class=\"\"><\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><strong>Regularized Gradient Boosting<\/strong><\/div>\n<\/div>\n<div class=\"\"><\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">It includes boosting with both L1 and L2 regularization.<\/div>\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><\/div>\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><a href=\"https:\/\/data-flair.training\/blogs\/machine-learning-algorithm\/\"><strong>Read more about Machine Learning Algorithms<\/strong><\/a><\/div>\n<\/div>\n<div class=\"\">\n<h4 class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">b. System Features<\/h4>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><em>For use of a range of computing environments this library provides:<\/em><\/div>\n<\/div>\n<div class=\"\">\n<ul>\n<li class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">Parallelization of tree construction using <span class=\"complexword\">all of<\/span> your CPU cores during training.<\/li>\n<\/ul>\n<\/div>\n<div class=\"\">\n<ul>\n<li class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">Distributed Computing for training very large models using a cluster of machines.<\/li>\n<\/ul>\n<\/div>\n<div class=\"\">\n<ul>\n<li class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">Out-of-Core Computing for very large datasets that don\u2019t fit into memory.<\/li>\n<\/ul>\n<\/div>\n<div class=\"\">\n<ul>\n<li class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">Cache Optimization of data structures and algorithm to make the best use of hardware.<\/li>\n<\/ul>\n<\/div>\n<div class=\"\">\n<h4 class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">c. Algorithm Features<\/h4>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">For efficiency of computing time and memory resources, we use XGBoost algorithm. Also, this <span class=\"passivevoice\">was designed<\/span> to make use of available resources to train the model.<\/div>\n<\/div>\n<div class=\"\"><\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><em>Some key algorithm implementation features include:<\/em><\/div>\n<\/div>\n<div class=\"\">\n<ul>\n<li class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">Sparse aware implementation with automatic handling of missing data values.<\/li>\n<\/ul>\n<\/div>\n<div class=\"\">\n<ul>\n<li class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">Block structure to support the parallelization of tree construction.<\/li>\n<\/ul>\n<\/div>\n<div class=\"\">\n<ul>\n<li class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">Continued training so that you can further boost an already fitted model on new data.<\/li>\n<\/ul>\n<\/div>\n<div class=\"\">\n<ul>\n<li class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">XGBoost is free open source software. That is available for use under the permissive Apache-2 license.<\/li>\n<li class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">XGBoost also uses complex methods of preventing overfitting, thus making it more reliable in implementation. This aspect is particularly notable when analyzing large data sets or data which is usually noisy, and containing many variables.<\/li>\n<\/ul>\n<\/div>\n<div class=\"\">\n<p class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><strong><a href=\"https:\/\/data-flair.training\/blogs\/machine-learning-applications\/\">Read about Applications of Machine Learning<\/a><\/strong><\/p>\n<h3 class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">XGBoost Tutorial &#8211; Why XGBoosting?<\/h3>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">The two reasons to use XGBoosting Algorithms are also the two goals of the project:<\/div>\n<\/div>\n<div class=\"\">\n<h4 class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">a. XGBoost Execution Speed<\/h4>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">When we compare XGBoosting to implementations of gradient boosting, it\u2019s so fast.<\/div>\n<\/div>\n<div class=\"\"><\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">It compares XGBoost to other implementations of gradient boosting and bagged decision trees. Also, he wrote up his results in May 2015 in the blog post titled. That is \u201cBenchmarking Random Forest Implementations\u201c.<\/div>\n<\/div>\n<div class=\"\"><\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">Moreover, it provides all the code on GitHub and a more extensive report of results with hard numbers.<\/div>\n<\/div>\n<div class=\"\">\n<h4 class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">b. XGBoost Model Performance<\/h4>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">It dominates structured datasets on classification and regression predictive modeling problems.<\/div>\n<\/div>\n<div class=\"\"><\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">The evidence is that it is a go-to algorithm for competition winners. That <span class=\"passivevoice\">is based<\/span> on the Kaggle competitive data science platform.<\/div>\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><\/div>\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><a href=\"https:\/\/data-flair.training\/blogs\/future-of-machine-learning\/\"><strong>Read more about\u00a0Reasons to learn Machine Learning<\/strong><\/a><\/div>\n<\/div>\n<div class=\"\">\n<h3 class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">XGBoost Tutorial &#8211; Why XGBoosting is good?<\/h3>\n<\/div>\n<div class=\"\">\n<h4 class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">a. Flexibility<\/h4>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">XGBoosting supports user-defined <span class=\"complexword\">objective<\/span> functions with classification, regression and ranking problems. We use an <span class=\"complexword\">objective<\/span> function to measure the performance of the model. That <span class=\"passivevoice\">is given<\/span> a certain set of parameters. Furthermore, it supports user-defined evaluation metrics as well.<\/div>\n<\/div>\n<div class=\"\">\n<h4 class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">b. Availability<\/h4>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">As it is available for programming languages such as<a href=\"https:\/\/data-flair.training\/blogs\/r-programming-functions\/\"><strong> R<\/strong><\/a>,<a href=\"https:\/\/data-flair.training\/blogs\/python-features\/\"><strong> Python<\/strong><\/a>,<a href=\"https:\/\/data-flair.training\/blogs\/features-of-java\/\"><strong> Java<\/strong><\/a>, Julia, and <a href=\"https:\/\/data-flair.training\/blogs\/partial-functions-scala-guide\/\"><strong>Scala<\/strong><\/a>.<\/div>\n<\/div>\n<div class=\"\">\n<h4 class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">c. Save and Reload<\/h4>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">We can <span class=\"adverb\">easily<\/span> save our data matrix and model and reload it later. Let suppose, if we have a large dataset, we can <span class=\"adverb\">simply<\/span> save the model. Further, we use it in future instead of wasting time redoing the computation.<\/div>\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><\/div>\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><a href=\"https:\/\/data-flair.training\/blogs\/advantages-and-disadvantages-of-machine-learning\/\"><strong>Read about Advantages &amp; Disadvantages of Machine Learning<\/strong><\/a><\/div>\n<\/div>\n<\/div>\n<div><\/div>\n<div>So, this was all about XGBoost Tutorial. Hope you like our explanation.<\/div>\n<div>\n<div class=\"\">\n<h3 class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">Conclusion<\/h3>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">XGBoost stands for Extreme Gradient Boosting. It is one of the fastest and most powerful algorithms for classification and regression tasks. XGBoost works just like regular gradient boosting but adds speed and better accuracy. It uses clever techniques like regularization, parallel processing, and tree pruning to make sure the model doesn\u2019t overfit and runs faster on big datasets.<\/div>\n<div><\/div>\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">Furthermore, if you have any queries, feel free to ask in the comment section.<\/div>\n<\/div>\n<\/div>\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><\/div>\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">See Also- <strong><a href=\"https:\/\/data-flair.training\/blogs\/artificial-neural-network\/\">Artificial Neural Network (ANN)<\/a><\/strong> &amp; <a href=\"https:\/\/data-flair.training\/blogs\/svm-support-vector-machine-tutorial\/\"><strong>Support Vector Machine (SVM)<\/strong><\/a><\/div>\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><\/div>\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><a href=\"https:\/\/en.wikipedia.org\/wiki\/Machine_learning\"><strong>For reference<\/strong><\/a><\/div>\n<div><\/div>\n","protected":false},"excerpt":{"rendered":"<p>In this XGBoost Tutorial, we will study What is XGBoosting. Also, will learn the features of XGBoosting and why we need\u00a0XGBoost Algorithm. 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