{"id":93825,"date":"2021-07-07T09:50:00","date_gmt":"2021-07-07T13:50:00","guid":{"rendered":"https:\/\/ibkrcampus.com\/?p=93825"},"modified":"2022-11-21T09:47:43","modified_gmt":"2022-11-21T14:47:43","slug":"how-to-plot-xgboost-trees-in-r","status":"publish","type":"post","link":"https:\/\/www.interactivebrokers.com\/campus\/ibkr-quant-news\/how-to-plot-xgboost-trees-in-r\/","title":{"rendered":"How to Plot XGBoost Trees in R"},"content":{"rendered":"\n

In this post, we\u2019re going to cover how to plot XGBoost trees in R. XGBoost<\/a> is a very popular machine learning algorithm, which is frequently used in Kaggle<\/a> competitions and has many practical use cases.<\/p>\n\n\n\n

Let\u2019s start by loading the packages we\u2019ll need. Note that plotting XGBoost trees requires the DiagrammeR<\/strong> package to be installed, so even if you have xgboost<\/strong> installed already, you\u2019ll need to make sure you have DiagrammeR<\/strong> also.<\/p>\n\n\n\n

\n# load libraries
\nlibrary(xgboost)
\nlibrary(caret)
\nlibrary(dplyr)
\nlibrary(DiagrammeR)\n<\/p>\n\n\n\n

Next, let\u2019s read in our dataset. In this post, we\u2019ll be using this customer churn dataset<\/a>. The label we\u2019ll be trying to predict is called \u201cExited\u201d and is a binary variable with 1 meaning the customer churned (canceled account) vs. 0 meaning the customer did not churn (did not cancel account).<\/p>\n\n\n\n

\n# read in dataset
\nchurn_data <- read.csv(\"Churn_Modelling.csv\")\n<\/p>\n\n\n\n

\"How<\/figure>\n\n\n\n

Preparing the dataset for modeling<\/strong><\/h2>\n\n\n\n

Now, let\u2019s prep our dataset for modeling. First, we\u2019ll remove a few variables we don\u2019t need. Second, we\u2019ll one hot encode each of the categorical variables. Then, we\u2019ll split our dataset into train and validation.<\/p>\n\n\n\n

\n# remove variables we won’t be using for modeling
\nneed_data <- churn_data %>% select(-RowNumber, -CustomerId, -Surname)

\n \n# one hot encode all categorical variables
\ndummy <- dummyVars(\" ~ .\", data = need_data)
\nneed_data <- data.frame(predict(dummy, newdata = need_data))

\n \ny_label <- need_data$Exited
\nneed_data <- need_data %>% select(-Exited)
\nneed_data <- data.frame(Exited = y_label, need_data)

\n \n# convert Exited column to a factor
\nneed_data$Exited <- as.factor(need_data$Exited)

\n \n# split data into train \/ validation
\nset.seed(0)
\nindexes <- sample(1:nrow(need_data), 0.7 * nrow(need_data))

\n \ntrain_data <- need_data[indexes,]
\nval_data <- need_data[-indexes,]\n<\/p>\n\n\n\n

Train the XGBoost model<\/strong><\/h2>\n\n\n\n

For the purpose of this article, we\u2019ll just vary a few of the hyperparameters using a grid search.<\/p>\n\n\n\n

\n# create tuning grid
\ngrid_default <- expand.grid(nrounds = c(50, 75, 100, 150, 200, 250),
\n max_depth = c(2, 3, 4, 5),
\n eta = c(0.05, 0.1, 0.15),
\n gamma = c(0),
\n colsample_bytree = c(0.7),
\n min_child_weight = c(5),
\n subsample = c(0.6))

\n \n# set random seed
\nset.seed(1234)

\n \n# train XGBoost model
\nxgb_model <- train(formula(need_data),
\n data = need_data,
\n tuneGrid = grid_default,
\n method = “xgbTree”,
\n metric = “Kappa”)\n<\/p>\n\n\n\n

Plotting XGBoost trees<\/strong><\/p>\n\n\n\n

Now, we\u2019re ready to plot some trees from the XGBoost model. We\u2019ll be able to do that using the xgb.plot.tree<\/em> function. Let\u2019s plot the first tree in the XGBoost ensemble. Note that in the code below, we specify the model object along with the index of the tree we want to plot.<\/p>\n\n\n\n

<\/p>\n\n\n\n

\n# plot the first tree
\nxgb.plot.tree(model = xgb_model$finalModel, trees = 1)\n<\/p>\n\n\n\n

\"How<\/figure>\n\n\n\n

From the plot, we can see that Age is used to make the first split in the tree.<\/p>\n\n\n\n

If we want to plot another tree, we can just change the tree index. For example, the line of code below plots the second tree in the XGBoost ensemble.<\/p>\n\n\n\n

\nxgb.plot.tree(model = xgb_model$finalModel, trees = 2)\n<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

If you want to adjust the height and width of the plot, you can do that by changing the plot_height<\/em> and plot_width<\/em> parameters:<\/p>\n\n\n\n

\nxgb.plot.tree(model = xgb_model$finalModel, trees = 1, plot_width = 1000, plot_height = 1000)\n<\/p>\n\n\n\n

Exporting tree plots to a file<\/strong><\/h4>\n\n\n\n

Plots can also be exported programmatically using the export_graph<\/em> function. Note that to do this, you\u2019ll need to set render = FALSE<\/em> in the xgb.plot.tree<\/em> function.<\/p>\n\n\n\n

\n# create plot object of XGBoost tree
\ntree_plot <- xgb.plot.tree(model = xgb_model$finalModel, trees = 1, plot_width = 1000,
\n plot_height = 1000, render = FALSE)

\n \n# export plot object to file
\nexport_graph(tree_plot, “xgboost_tree_plot.pdf”, width = 1000, height = 1000)\n<\/p>\n\n\n\n

Plotting multiple trees at once<\/strong><\/h4>\n\n\n\n

Rather than plotting a single tree, you can also plot multiple trees at once. You just need to pass a vector of which trees you\u2019d like to plot. For example, the code below will plot the first three trees.<\/p>\n\n\n\n

\nxgb.plot.tree(model = xgb_model$finalModel, trees = 1:3)\n<\/p>\n\n\n\n

Visit TheAutomatic.net Blog for additional insight on this topic: https:\/\/theautomatic.net\/2021\/04\/28\/how-to-plot-xgboost-trees-in-r\/<\/a> <\/p>\n","protected":false},"excerpt":{"rendered":"

Learn how to plot XGBoost trees in R, and get sample code for using caret, dplyr and DiagrammeR packages.<\/p>\n","protected":false},"author":388,"featured_media":44292,"comment_status":"closed","ping_status":"open","sticky":true,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[339,343,338,341,352,344,342],"tags":[2532,806,9967,487,6591,9966],"contributors-categories":[13695],"class_list":{"0":"post-93825","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-data-science","8":"category-programing-languages","9":"category-ibkr-quant-news","10":"category-quant-development","11":"category-quant-north-america","12":"category-quant-regions","13":"category-r-development","14":"tag-caret","15":"tag-data-science","16":"tag-dplyr-and-diagrammer","17":"tag-r","18":"tag-rstats","19":"tag-xgboost","20":"contributors-categories-theautomatic-net"},"pp_statuses_selecting_workflow":false,"pp_workflow_action":"current","pp_status_selection":"publish","acf":[],"yoast_head":"\nHow to Plot XGBoost 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