1. Learn
    2. /
  2. Courses
    3. /
  3. Extreme Gradient Boosting with XGBoost

Exercise

Visualizing individual XGBoost trees

Now that you've used XGBoost to both build and evaluate regression as well as classification models, you should get a handle on how to visually explore your models. Here, you will visualize individual trees from the fully boosted model that XGBoost creates using the entire housing dataset.

XGBoost has a plot_tree() function that makes this type of visualization easy. Once you train a model using the XGBoost learning API, you can pass it to the plot_tree() function along with the number of trees you want to plot using the num_trees argument.

Instructions

100 XP
  • Create a parameter dictionary with an "objective" of "reg:linear" and a "max_depth" of 2.
  • Train the model using 10 boosting rounds and the parameter dictionary you created. Save the result in xg_reg.
  • Plot the first tree using xgb.plot_tree(). It takes in two arguments - the model (in this case, xg_reg), and num_trees, which is 0-indexed. So to plot the first tree, specify num_trees=0.
  • Plot the fifth tree.
  • Plot the last (tenth) tree sideways. To do this, specify the additional keyword argument rankdir="LR".