What Are Decision Tree Interview Questions?

When interviewing for a job in data science or machine learning, you may encounter questions related to the decision tree, an algorithm that helps you visualize decisions and predicts the problems that may arise as a result of them. Whether the goal is to solve a difficult issue or make an educated guess about a future outcome, the decision tree is a staple of the machine learning industry because it’s simple to follow and quick to understand. Developing a strong grasp of decision trees can assist you in many fields because other industries that utilize decision trees are the finance, health care, and marketing sectors. Explore the basics of the decision tree, including common decision tree interview questions and how to prepare for them.

Basic concepts of decision trees

It’s important for you to grasp the fundamentals of the decision tree, such as root nodes and leaf nodes, before getting into specific interview questions about the algorithm. From their root nodes to their criteria for splitting, comprehending these key elements of the decision tree will grant you the ability to apply them in real-world scenarios, helping you ace decision tree interview questions.

Nodes

Before implementing a decision tree to assist in the decision-making process, you need to understand that they consist of three main types of nodes:

• Root node: This is the starting point of the decision tree. All decision-making data branches out from this root node. There should only be one root node, and there should be at least two decision nodes that split from it.

• Decision node: These occur when internal nodes split into paths based on possible choices that link back to the root node. Decision nodes split into sub nodes.

• Leaf node: The final outcome or ultimate decision of a branch of the decision tree. These nodes don’t split any further.

Criteria for splitting

In order to move from one node to the next, an act known as splitting, specific criteria need to be met for the data to be divided. Some of the most common criteria for splitting include:

• Information gain: One reason a decision tree might divide at the decision node is due to information gain, a measure of the effectiveness of a feature in classifying data. It’s closely related to the concept of entropy, which measures the potential amount of randomness in the data. Think of it like flipping a coin: Whether the coin will land heads or tails is completely random, but a decision tree would need nodes for both potential outcomes to account for information gain. If a branch in the decision tree is at zero entropy—no more possibilities—you have reached a leaf node.

• Gini impurity: Another potential reason for a node to split concerns the Gini index—a measure of how likely it would be to misclassify randomly chosen data. For example, if a decision tree were being used to teach a machine learning model to discern between apples and oranges, a decision node relating to shape would likely lead to more misclassifications than a decision node relating to color.

Common decision tree interview questions

Explore some common decision tree interview questions you may encounter in a job interview in the data science and machine learning industries. Each question aims to gauge your understanding of decision tree algorithms and their practical applications in the field.

How is a decision tree split?

• What they’re really asking: This question is trying to determine whether you understand basic decision tree structure, including how to create nodes and make data-driven splits.

• Other forms of this question you may encounter: 

  • “Walk me through the steps for building a decision tree.” 

  • “Explain how to construct a decision tree.”

  • “Tell me about the factors that affect the structure of a decision tree.”

What are the advantages and disadvantages of using a decision tree?

• What they’re really asking: The interviewer wants to know if you’re aware of the right scenarios in which to use a decision tree. They’re also curious about whether you can recognize the limitations of the algorithm.

• Other forms of this question you may encounter:

  • “When might you choose not to use a decision tree?”

  • “Tell me some alternatives to using decision trees.”

  • “Explain the pros and cons of a decision tree.”

How would you prevent inaccuracies in a decision tree?

• What they’re really asking: The hiring manager wants to test your knowledge of decision tree techniques. They want to see if you’d be able to make a decision tree’s nodes more accurate without increasing the algorithm’s Gini impurity.

• Other forms of this question you may encounter:

  • “What methods exist to help ensure a decision tree generalizes new data accurately?”

  • “How might you account for Gini impurity in a decision tree algorithm?”

  • “Tell me about a time when you had to deal with misclassified data in a decision tree.”

Advanced decision tree interview questions

During the interview process, the hiring manager may ask more advanced decision tree interview questions to get a better idea of your technical expertise with the algorithm.

What’s the difference between CART, ID3, and C4.5 decision trees?

• What they’re really asking: This question is meant to gauge your knowledge of different types of decision trees and your ability to discern when to use each type.

• Other forms of this question you may encounter:

  • “When would you use a CART algorithm?”

  • “What is an ID3 decision tree?”

  • “How would you rely on the C4.5 algorithm in your work?”

Explain how to calculate feature importance in a decision tree.

• What they’re really asking: Because decision trees typically rank features based on their importance in the classification process, the interviewer needs to know how well you understand information gain and the Gini index.

• Other forms of this question you may encounter:

  • “How would you avoid overfitting in a decision tree?”

  • “When it comes to decision tree algorithms, would you say that the simplest explanation is usually the best?”

  • “Tell me about your understanding of Occam’s Razor.”

Preparing for decision tree interview questions

If you want to tackle decision tree interview questions confidently, you need to give yourself time to prepare. This includes reviewing the theory of decision trees, refreshing yourself on the different decision tree algorithms (and their use cases), and creating practice trees with real-world data. The following strategies can also help you prepare:

Study decision tree algorithms

Take the appropriate time to understand CART, ID3, and C4.5. Consider their differences so that you will know when to apply each. You can convince the hiring manager of your abilities by not only conveying your understanding of these concepts but also by displaying your knowledge about how to use them.

Practice with data sets

Practice your knowledge of decision trees using any data you’ve collected. This will give you real-world experience with the visual framework—not to mention the chance to uncover new insights you might not have picked up on before because a decision tree requires you to view an issue from multiple angles.

Getting started with decision trees on Coursera

Decision tree knowledge is important for a role in data science or machine learning. If you need to deepen your skills, Coursera's courses can help. 

Get an introduction to machine learning—including algorithms, applications, and data preparation—in the Machine Learning Specialization provided by Stanford on Coursera. You might also consider AI For Everyone offered by DeepLearning.AI on Coursera, which can help you learn more about artificial intelligence, including decision trees and other data-driven algorithms.