How to plot the decision trees from XGBoost classifier
In this article, I am going to show you how to plot the decision trees generated by XGBoost models. First, we have to install graphviz (both python library and executable files)
1 2 !pip install graphviz !apt-get install graphviz
When the graphviz library is installed, we can train an XGBoost model (in this example, I am going to train it using the Titanic dataset).
1 2 3 4 5 6 7 8 from xgboost import XGBClassifier from xgboost import plot_tree #(...) other imports #(...) loading the dataset and data preprocessing model = XGBClassifier() model.fit(X_train, y_train, verbose=True, eval_set=[(X_test, y_test)])
To display the trees, we have to use the plot_tree function provided by XGBoost.
It is important to change the size of the plot because the default one is not readable. The num_trees indicates the tree that should be drawn not the number of trees, so when I set the value to two, I get the second tree generated by XGBoost.
1 2 3 plot_tree(model, num_trees=1) fig = plt.gcf() fig.set_size_inches(30, 15)
You may also like
- Forward feature selection in Scikit-Learn
- A few useful things to know about machine learning
- Generalized Linear Models — Using linear regression when the dependent variable does not follow Gaussian distribution
- What is the difference between training, validation, and test sets in machine learning
- Precision vs. recall - explanation