What are the advantages of using decision trees over other models, and how does Scikit-learn visualize them for understanding?

What are some key advantages of using decision trees in comparison to other machine learning models, particularly in terms of transparency and interpretability? Also, could you elaborate on how decision trees are visualized using Scikit-learn, and why such visualization is beneficial for understanding the decision-making process?


Decision trees indeed offer a transparent and interpretable way to understand the decision-making process in machine learning. They present a hierarchical structure of decision rules, starting from a single node (the root node) that represents the entire dataset. As the tree branches out based on different values of input variables, each branch represents a decision rule.

At each decision node, the tree poses a question about a specific feature and assigns data points to different branches based on their answers. This results in a sequence of if-else statements, which are easily interpretable by humans.

Regarding visualizing decision trees with Scikit-learn, here’s an example using the iris dataset:

In this example, after loading the iris dataset, we create a DecisionTreeClassifier object and fit it to the data. Finally, we use plot_tree to visualize the decision tree, with the filled=True argument providing color-coded nodes for better interpretation.