{"id":6972,"date":"2018-02-01T09:04:06","date_gmt":"2018-02-01T03:34:06","guid":{"rendered":"https:\/\/data-flair.training\/blogs\/?p=6972"},"modified":"2025-07-28T15:40:02","modified_gmt":"2025-07-28T10:10:02","slug":"xgboost-algorithm","status":"publish","type":"post","link":"https:\/\/data-flair.training\/blogs\/xgboost-algorithm\/","title":{"rendered":"What is XGBoost Algorithm &#8211; Applied Machine Learning"},"content":{"rendered":"<div class=\"\">\n<p>In this <strong><a href=\"https:\/\/data-flair.training\/blogs\/machine-learning-tutorial\/\">Machine Learning Tutorial<\/a><\/strong>, we will learn Introduction to XGBoost, coding of XGBoost Algorithm, an Advanced functionality of XGboost Algorithm, General Parameters, Booster Parameters, Linear Booster Specific Parameters, Learning Task Parameters. Furthermore, we will study building models and parameters of XGBoost.<\/p>\n<p>So, let&#8217;s start the XGBoost Algorithm Tutorial.<\/p>\n<h3>Introduction to XGBoost Algorithm<\/h3>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><span class=\"adverb\">Basically<\/span>, XGBoost is an algorithm. Also, it has recently been dominating applied machine learning. XGBoost is an implementation of <strong><a href=\"https:\/\/data-flair.training\/blogs\/gradient-boosting-algorithm\/\">gradient boosted <\/a><\/strong>decision trees. Although, it <span class=\"passivevoice\">was designed<\/span> for speed and performance.\u00a0<span class=\"adverb\">Basically<\/span>, it 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 class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><\/div>\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><strong><a href=\"https:\/\/data-flair.training\/blogs\/what-is-xgboost\/\">Read more in detail, What is XGBoost in Machine Learning<\/a><\/strong><\/div>\n<div><\/div>\n<h3 class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">XGBoost Algorithm working With Main Interfaces<\/h3>\n<\/div>\n<div class=\"\">\n<ul>\n<li class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">C++, <a href=\"https:\/\/data-flair.training\/blogs\/java-tutorial\/\"><strong>Java<\/strong><\/a> and JVM languages.<\/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\">Command Line Interface.<\/li>\n<\/ul>\n<\/div>\n<div class=\"\">\n<ul>\n<li class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><strong><a href=\"https:\/\/data-flair.training\/blogs\/python-tutorial-for-beginners\/\">Python<\/a><\/strong> interface along with integrated model in scikit-learn.<\/li>\n<\/ul>\n<\/div>\n<div class=\"\">\n<ul>\n<li class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><strong><a href=\"https:\/\/data-flair.training\/blogs\/r-programming-language\/\">R<\/a><\/strong> interface as well as a model in the caret package.<\/li>\n<\/ul>\n<\/div>\n<div class=\"\">\n<p 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><\/p>\n<h3 class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">Preparation of Data for using XGBoost Algorithm<\/h3>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">Let\u2019s assume, you have a dataset named \u2018campaign\u2019 . If want to convert all categorical variables into such flags. Then except the response variable. Here is how you do it :<\/div>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">sparse_matrix &lt;- sparse.model.matrix(response ~ .-1, data = campaign)<\/div>\n<\/div>\n<div class=\"\"><\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><strong>Now let\u2019s break down this code as follows:<\/strong><\/div>\n<\/div>\n<div class=\"\"><\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">\u201csparse.model.matrix\u201d is the command. And, all other inputs inside parentheses are parameters.<\/div>\n<\/div>\n<div class=\"\"><\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">The parameter \u201cresponse\u201d says that this statement should ignore \u201cresponse\u201d variable.<\/div>\n<\/div>\n<div class=\"\"><\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">\u201c-1\u201d removes an extra column which this command creates as the first column.<\/div>\n<\/div>\n<div class=\"\"><\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">And finally, you specify the dataset name.<\/div>\n<\/div>\n<div class=\"\"><\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">To convert the target variables as well, you can use following code:<\/div>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">output_vector = df[,response] == &#8220;Responder&#8221;<\/div>\n<\/div>\n<div class=\"\"><\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><strong>Here is what the code does:<\/strong><\/div>\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><\/div>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">set output_vector to 0<\/div>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">set output_vector to 1 for rows where a response is &#8220;Responder&#8221; is TRUE ;<\/div>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">return output_vector.<\/div>\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><\/div>\n<div 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><\/div>\n<\/div>\n<div class=\"\">\n<h3 class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">Building Model &#8211; Xgboost AlgorithmR<\/h3>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">Here are simple steps you can use to crack any data problem using xgboost Algorithm:<\/div>\n<\/div>\n<div class=\"\">\n<h4 class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">Step 1: Load all the libraries<\/h4>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">library(xgboost)<\/div>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">library(readr)<\/div>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">library(stringr)<\/div>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">library(caret)<\/div>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">library(car)<\/div>\n<\/div>\n<div class=\"\">\n<h4 class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">Step 2: Load the dataset<\/h4>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">(Here I use a bank data where we need to find whether a customer is eligible for loan or not).<\/div>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">set.seed(100)<\/div>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">setwd(&#8220;C:\\\\Users\\\\ts93856\\\\Desktop\\\\datasource&#8221;)<\/div>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"># load data<\/div>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">df_train = read_csv(&#8220;train_users_2.csv&#8221;)<\/div>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">df_test = read_csv(&#8220;test_users.csv&#8221;)<\/div>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"># Loading labels of train data<\/div>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">labels = df_train[&#8216;labels&#8217;]<\/div>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">df_train = df_train[-grep(&#8216;labels&#8217;, colnames(df_train))]<\/div>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"># combine train and test data<\/div>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">df_all = rbind(df_train,df_test)<\/div>\n<\/div>\n<div class=\"\">\n<h4 class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">Step 3: Data Cleaning &amp; Feature Engineering<\/h4>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"># clean Variables: here I clean people with age less than 14 or more than 100<\/div>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">df_all[df_all$age &lt; 14 | df_all$age &gt; 100,&#8217;age&#8217;] &lt;- -1<\/div>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">df_all$age[df_all$age &lt; 0] &lt;- mean(df_all$age[df_all$age &gt; 0])<\/div>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"># one-hot-encoding categorical features<\/div>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">ohe_feats = c(&#8216;gender&#8217;, &#8216;education&#8217;, &#8217;employer&#8217;)<\/div>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">dummies &lt;- dummyVars(~ gender + education + employer, data = df_all)<\/div>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">df_all_ohe &lt;- as.data.frame(predict(dummies, newdata = df_all))<\/div>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><span class=\"veryhardreadability\">df_all_combined &lt;- cbind(df_all[,-c(which(colnames(df_all) %in% ohe_feats))],df_all_ohe)df_all_combined$agena &lt;- as.factor(ifelse(df_all_combined$age &lt; 0,1,0))<\/span><\/div>\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><\/div>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">I am using a list of variables in \u201cfeature_selected\u201d to <span class=\"passivevoice\">be used by<\/span> the model. I have shared a quick and smart way to choose variables later in this article.<\/div>\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><\/div>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">df_all_combined &lt;- df_all_combined[,c(&#8216;id&#8217;,features_selected)]<\/div>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"># split train and test<\/div>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">X = df_all_combined[df_all_combined$id %in% df_train$id,]<\/div>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">y &lt;- recode(labels$labels,&#8221;&#8216;True&#8217;=1; &#8216;False&#8217;=0)<\/div>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">X_test = df_all_combined[df_all_combined$id %in% df_test$id,]<\/div>\n<\/div>\n<div class=\"\">\n<h4 class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">Step 4: Tune and Run the model<\/h4>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">xgb &lt;- xgboost(data = data.matrix(X[,-1]),<\/div>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">label = y,<\/div>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">eta = 0.1,<\/div>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">max_depth = 15,<\/div>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">nround=25,<\/div>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">subsample = 0.5,<\/div>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">colsample_bytree = 0.5,<\/div>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">seed = 1,<\/div>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">eval_metric = &#8220;merror&#8221;,<\/div>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><span class=\"complexword\">objective<\/span> = &#8220;multi:softprob&#8221;,<\/div>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">num_class = 12,<\/div>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">nthread = 3<\/div>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">)<\/div>\n<\/div>\n<div class=\"\"><\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">Step 5: Score the Test Population<\/div>\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><\/div>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">And that\u2019s it! You now have an object \u201cxgb\u201d which is an xgboost Algorithm model. Here is how you score a test population :<\/div>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"># predict values in test set<\/div>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">y_pred &lt;- predict(xgb, data.matrix(X_test[,-1]))<\/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 Algorithm &#8211; Parameters<\/h3>\n<\/div>\n<h4 class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">a. General Parameters<\/h4>\n<p>Following are the General parameters used in Xgboost Algorithm:<\/p>\n<div class=\"\">\n<ul>\n<li class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><strong>silent:<\/strong> The default value is 0. You need to specify 0 for printing running messages, 1 for silent mode.<\/li>\n<\/ul>\n<\/div>\n<div class=\"\">\n<ul>\n<li class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><strong>booster:<\/strong> The default value is gbtree. You need to specify the booster to use: gbtree (tree based) or gblinear (linear function).<\/li>\n<\/ul>\n<\/div>\n<div class=\"\">\n<ul>\n<li class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><strong>num_pbuffer:<\/strong> This is set <span class=\"adverb\">automatically<\/span> by xgboost Algorithm, no need to be set by a user. Read the documentation of xgboost for more details.<\/li>\n<\/ul>\n<\/div>\n<div class=\"\">\n<ul>\n<li class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><strong>num_feature:<\/strong> This is set <span class=\"adverb\">automatically<\/span> by xgboost Algorithm, no need to be set by a user.<\/li>\n<\/ul>\n<\/div>\n<h4 class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">b. Booster Parameters<\/h4>\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">Below we discussed tree-specific parameters in\u00a0Xgboost Algorithm:<\/div>\n<div class=\"\">\n<ul>\n<li class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><strong>eta:<\/strong> The default value is set to 0.3. You need to specify step size shrinkage used in an update to prevents overfitting. After each boosting step, we can <span class=\"adverb\">directly<\/span> get the weights of new features. eta actually shrinks the feature weights to make the boosting process more conservative. The range is 0 to 1. Low eta value means the model is more robust to overfitting.<\/li>\n<\/ul>\n<\/div>\n<div class=\"\">\n<ul>\n<li class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><strong>gamma:<\/strong> The default value is set to 0. You need to specify <span class=\"complexword\">minimum<\/span> loss reduction required to make a further partition on a leaf node of the tree. The larger, the more conservative the algorithm will be. The range is 0 to \u221e. Larger the gamma more conservative the algorithm is.<\/li>\n<\/ul>\n<\/div>\n<div class=\"\">\n<ul>\n<li class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><strong>max_depth:<\/strong> The default value is set to 6. You need to specify the <span class=\"complexword\">maximum<\/span> depth of a tree. The range is 1 to \u221e.<\/li>\n<\/ul>\n<\/div>\n<div class=\"\">\n<ul>\n<li class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><strong>min_child_weight:<\/strong> The default value is set to 1. You need to specify the <span class=\"complexword\">minimum<\/span> sum of instance weight(hessian) needed in a child. If the tree partition step results in a leaf node. Then with the sum of instance weight less than min_child_weight. Then the building process will give up further partitioning. In linear regression mode, corresponds to a <span class=\"complexword\">minimum<\/span> number of instances needed to be in each node. The larger, the more conservative the algorithm will be. The range is 0 to \u221e.<\/li>\n<\/ul>\n<\/div>\n<div class=\"\">\n<ul>\n<li class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><strong>max_delta_step:<\/strong> The default value is set to 0. <span class=\"complexword\">Maximum<\/span> delta step we allow each tree\u2019s weight estimation to be. If the value is set to 0, it means there is no constraint. If it is set to a positive value, it can help make the update step more conservative. Usually, this parameter is not needed, but it might help in logistic regression. <span class=\"adverb\">Especially<\/span>, when a class is <span class=\"adverb\">extremely<\/span> imbalanced. Set it to a value of 1-10 might help control the update.The range is 0 to \u221e.<\/li>\n<\/ul>\n<\/div>\n<div class=\"\">\n<ul>\n<li class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><strong>subsample:<\/strong> The default value is set to 1. You need to specify the subsample ratio of the training instance. Setting it to 0.5 means that XGBoost <span class=\"adverb\">randomly<\/span> collected half of the data instances. That needs to grow trees and this will prevent overfitting. The range is 0 to 1.<\/li>\n<\/ul>\n<\/div>\n<div class=\"\">\n<ul>\n<li class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><strong>colsample_bytree:<\/strong> The default value is set to 1. You need to specify the subsample ratio of columns when constructing each tree. The range is 0 to 1.<\/li>\n<\/ul>\n<\/div>\n<h4 class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">c. Linear Booster Specific Parameters<\/h4>\n<p>These are Linear Booster Specific Parameters in XGBoost Algorithm.<\/p>\n<div class=\"\">\n<ul>\n<li class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><strong>lambda and alpha:<\/strong> These are regularization term on weights. Lambda default value assumed is 1 and alpha are 0.<\/li>\n<\/ul>\n<\/div>\n<div class=\"\">\n<ul>\n<li class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><strong>lambda_bias:<\/strong> L2 regularization term on bias and has a default value of 0.<\/li>\n<\/ul>\n<\/div>\n<h4 class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">d. Learning Task Parameters<\/h4>\n<p>Following are the Learning Task Parameters in XGBoost Algorithm<\/p>\n<div class=\"\">\n<ul>\n<li class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><strong>base_score:<\/strong> The default value is set to 0.5. You need to specify the initial prediction score of all instances, global bias.<\/li>\n<\/ul>\n<\/div>\n<div class=\"\">\n<ul>\n<li class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><strong>objective:<\/strong> The default value is set to reg:linear. You need to specify the type of learner you want. That includes linear regression, Poisson regression etc.<\/li>\n<\/ul>\n<\/div>\n<div class=\"\">\n<ul>\n<li class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><strong>eval_metric:<\/strong> You need to specify the evaluation metrics for validation data. And a default metric will <span class=\"passivevoice\">be assigned<\/span> according to the <span class=\"complexword\">objective<\/span>.<\/li>\n<\/ul>\n<\/div>\n<div class=\"\">\n<ul>\n<li class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><strong>seed:<\/strong> As always here you specify the seed to reproduce the same set of outputs.<\/li>\n<\/ul>\n<\/div>\n<p 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><\/p>\n<h3 class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">Advanced functionality of XGBoost Algorithm<\/h3>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">We can say xgboost is simple in comparison to other machine learning techniques. If you did all we have done till now, you already have a model.<\/div>\n<\/div>\n<div class=\"\"><\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">Let\u2019s take it one step further and try to find the variable importance in the model and subset our variable list.<\/div>\n<\/div>\n<div class=\"\"><\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"># Lets start with finding what the actual tree looks like<\/div>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">model &lt;- xgb.dump(xgb, with.stats = T)<\/div>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">model[1:10] #This statement prints top 10 nodes of the model<\/div>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"># Get the feature real names<\/div>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">names &lt;- dimnames(data.matrix(X[,-1]))[[2]]<\/div>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"># Compute feature importance matrix<\/div>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">importance_matrix &lt;- xgb.importance(names, model = xgb)<\/div>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"># Nice graph<\/div>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">xgb.plot.importance(importance_matrix[1:10,])<\/div>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">#In case last step does not work for you because of a version issue, you can try following :<\/div>\n<\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">barplot(importance_matrix[,1])<\/div>\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\"><\/div>\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">Learn something new today in Machine Learning\u00a0\u00a0<strong><a href=\"https:\/\/data-flair.training\/blogs\/artificial-neural-network\/\">Artificial Neural Network (ANN)<\/a><\/strong><\/div>\n<\/div>\n<div><\/div>\n<div>So, this was all about\u00a0XGBoost Algorithm Tutorial. Hope you like our explanation.<\/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\">As a result, we have studied Introduction to XGBoost. Also, have learned about the coding of XGBoost Algorithm, an Advanced functionality of XGboosting Algorithm, General Parameters, Booster Parameters, Linear Booster Specific Parameters, Learning Task Parameters. As we have also seen about building models and parameters of XGBoost.<\/div>\n<\/div>\n<div><\/div>\n<div>The XGBoost algorithm builds decision trees one by one and each new tree corrects the mistakes of the earlier ones. What makes XGBoost special is that it adds regularization to reduce overfitting.<\/div>\n<div><\/div>\n<div class=\"\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\">Furthermore, if you have any queries, feel free to ask in comment section.<\/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\/svm-kernel-functions\/\">Kernel Function <\/a><\/strong>&amp; <strong><a href=\"https:\/\/data-flair.training\/blogs\/deep-learning\/\">Deep Learning<\/a><\/strong><\/div>\n<\/div>\n<div class=\"\"><\/div>\n<div class=\"\"><a href=\"https:\/\/en.wikipedia.org\/wiki\/Machine_learning\"><strong>For reference<\/strong><\/a><\/div>\n<p><span hidden class=\"__iawmlf-post-loop-links\" data-iawmlf-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 07:53:29&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2025-12-15 08:47:56&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2025-12-18 09:10:23&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2025-12-21 10:50:59&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2025-12-25 05:53:38&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2025-12-28 07:50:19&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2025-12-31 09:45:43&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-01-03 11:19:19&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-01-06 11:20:39&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-01-09 12:30:49&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-01-12 13:21:45&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-01-15 16:42:09&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-01-19 02:06:11&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-01-22 05:26:32&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-01-25 06:14:05&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-01-28 06:53:55&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-01-31 08:50:06&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-02-03 10:51:09&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-02-06 14:16:56&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-02-09 16:05:14&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-02-12 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15:25:55&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-03-28 22:44:26&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-04-01 08:22:35&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-04-04 10:30:37&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-04-07 10:41:53&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-04-10 19:54:58&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-04-14 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;date&quot;:&quot;2026-06-06 01:41:18&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-06-09 05:37:43&quot;,&quot;http_code&quot;:404},{&quot;date&quot;:&quot;2026-06-12 11:02:59&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-06-15 14:53:36&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-06-19 01:26:03&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-06-22 06:01:25&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-06-25 06:46:24&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-06-28 12:09:57&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-07-01 13:14:40&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-07-05 00:29:44&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-07-08 01:05:51&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-07-11 09:35:31&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-07-14 11:02:50&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-07-18 02:59:31&quot;,&quot;http_code&quot;:200}],&quot;broken&quot;:false,&quot;last_checked&quot;:{&quot;date&quot;:&quot;2026-07-18 02:59:31&quot;,&quot;http_code&quot;:200},&quot;process&quot;:&quot;done&quot;}]\"><\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>In this Machine Learning Tutorial, we will learn Introduction to XGBoost, coding of XGBoost Algorithm, an Advanced functionality of XGboost Algorithm, General Parameters, Booster Parameters, Linear Booster Specific Parameters, Learning Task Parameters. Furthermore, we&#46;&#46;&#46;<\/p>\n","protected":false},"author":5,"featured_media":42304,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[36],"tags":[2162,5041,8195,14905,16483,16304,16305,16307,16308],"class_list":["post-6972","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-machine-learning","tag-booster-parameters","tag-general-parameters","tag-learning-task-parameters","tag-tree-boosting-with-xgboost","tag-xgboost-algorithm-introduction","tag-xgboost-algorithms","tag-xgboost-archives","tag-xgboosting","tag-xgboosting-algorithms"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.0 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>What is XGBoost Algorithm - Applied Machine Learning - DataFlair<\/title>\n<meta name=\"description\" content=\"What is XGBoost Algorithm-Preparation of Data with XGBoost,Building Model using Xgboosting on R,Parameters used in Xgboost,Advanced functionality of xgboost\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/data-flair.training\/blogs\/xgboost-algorithm\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"What is XGBoost Algorithm - Applied Machine Learning - DataFlair\" \/>\n<meta property=\"og:description\" content=\"What is XGBoost Algorithm-Preparation of Data with XGBoost,Building Model using Xgboosting on R,Parameters used in Xgboost,Advanced functionality of xgboost\" \/>\n<meta property=\"og:url\" content=\"https:\/\/data-flair.training\/blogs\/xgboost-algorithm\/\" \/>\n<meta property=\"og:site_name\" content=\"DataFlair\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/DataFlairWS\/\" \/>\n<meta property=\"article:published_time\" content=\"2018-02-01T03:34:06+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2025-07-28T10:10:02+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/02\/XGBoost-1.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"1200\" \/>\n\t<meta property=\"og:image:height\" content=\"628\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"author\" content=\"DataFlair Team\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@DataFlairWS\" \/>\n<meta name=\"twitter:site\" content=\"@DataFlairWS\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"DataFlair Team\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"7 minutes\" \/>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"What is XGBoost Algorithm - Applied Machine Learning - DataFlair","description":"What is XGBoost Algorithm-Preparation of Data with XGBoost,Building Model using Xgboosting on R,Parameters used in Xgboost,Advanced functionality of xgboost","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/data-flair.training\/blogs\/xgboost-algorithm\/","og_locale":"en_US","og_type":"article","og_title":"What is XGBoost Algorithm - Applied Machine Learning - DataFlair","og_description":"What is XGBoost Algorithm-Preparation of Data with XGBoost,Building Model using Xgboosting on R,Parameters used in Xgboost,Advanced functionality of xgboost","og_url":"https:\/\/data-flair.training\/blogs\/xgboost-algorithm\/","og_site_name":"DataFlair","article_publisher":"https:\/\/www.facebook.com\/DataFlairWS\/","article_published_time":"2018-02-01T03:34:06+00:00","article_modified_time":"2025-07-28T10:10:02+00:00","og_image":[{"width":1200,"height":628,"url":"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/02\/XGBoost-1.jpg","type":"image\/jpeg"}],"author":"DataFlair Team","twitter_card":"summary_large_image","twitter_creator":"@DataFlairWS","twitter_site":"@DataFlairWS","twitter_misc":{"Written by":"DataFlair Team","Est. reading time":"7 minutes"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"Article","@id":"https:\/\/data-flair.training\/blogs\/xgboost-algorithm\/#article","isPartOf":{"@id":"https:\/\/data-flair.training\/blogs\/xgboost-algorithm\/"},"author":{"name":"DataFlair Team","@id":"https:\/\/data-flair.training\/blogs\/#\/schema\/person\/7f83c342f5d1632d6f7b4b0b0f447823"},"headline":"What is XGBoost Algorithm &#8211; 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