Decision Tree Interview Questions & Answers [For Beginners & Experienced]

In the world of machine learning, decision trees are by one of them, if not the most respectable, algorithm. Decision trees are mighty as well. Decision trees are used to both predict the continuous values (regression) or predict classes (perform classification or classify) of the instances provided to the algorithm.

Practicing decision tree interview questions beforehand can significantly increase your chances of nailing that knowledge-based round. Interview questions on decision tree can range in varied types, starting from basic explanatory ones to choosing the right statement from the pile. 

Decision trees are similar to a flowchart in its structure. The node of any decision tree represents a test done on the attribute. Each and every branch of the decision tree is representative of the results of the examination conducted on each node. The node of every leaf (which is also known as terminal nodes) holds the label of the class. 

That was about the structure of the tree; however, the surge in decision trees’ popularity is not due to the way they are created. The tree’s transparency gives it standing of its own in the world dominated with powerful and useful algorithms. You can actually do everything by hand for a small decision tree, and you can predict how the decision tree would be formed. For trees that are larger in size, this exercise becomes quite tedious.

However, that does not mean that you will not be able to understand what the tree is doing at each node. The ability to grasp what is happening behind the scenes or under the hood really differentiates decision trees with any other machine learning algorithm out there.

As we have seen how vital decision trees are, it is inherent that decision trees would also be critical for any machine learning professional or data scientist. To help you understand this concept and at the same time to help you get that extra zing in your interview flair, we have made a comprehensive list of decision tree interview questions and decision tree interview questions and answers.

These questions should help you ace any interview. By combining these questions and answers, you will be able to make your very own decision tree questions and answers PDF. Try to solve each of these questions first before reading the solutions to gain the most out of these questions.

Decision Tree Interview Questions & Answers

Q1. You will see two statements listed below. You will have to read both of them carefully and then choose one of the options from the two statements. The contextual question is, choose the statements that are true about bagging trees.

1. The individual trees are not at all dependent on each other for a bagging tree.
2. To improve the overall performance of the model, the aggregate is taken from weak learners. This method is known as bagging trees.
3. Only statement number one is TRUE.
4. Only statement number two is TRUE.
5. Both statements one and two are TRUE.
6. None of the options which are mentioned above.

Ans. The correct answer to this question is C because, for a bagging tree, both of these statements are true. In bagging trees or bootstrap aggregation, the main goal of applying this algorithm is to reduce the amount of variance present in the decision tree. The mechanism of creating a bagging tree is that, with replacement, a number of subsets are taken from the sample present for training the data.

Now, each of these smaller subsets of data is used to train a separate decision tree. Since the information which is fed into each tree comes out to be unique, the likelihood of any tree having any impact on the other becomes very low. The final result which all these trees give is collected and then processed to provide the output. Thus, the second statement also comes out to be true.

Q2. You will see two statements listed below. You will have to read both of them carefully and then choose one of the options from the two statements. The contextual question is, choose the statements that are true about boosting trees.

1. The weak learners in a boosting tree are independent of each other.
2. The weak learners’ performance is all collected and aggregated to improve the boosted tree’s overall performance.
3. Only statement number one is TRUE.
4. Only statement number two is TRUE.
5. Both statements one and two are TRUE.
6. None of the options which are mentioned above.

Ans. If you were to understand how the boosting of trees is done, you would understand and be able to differentiate the correct statement from the statement, that is false. So, a boosted tree is created when many weak learners are connected in series. Each tree present in this sequence has one sole aim: to reduce the error that its predecessor made.

If the trees are connected in such fashion, all the trees cannot be independent of each other, thus rendering the first statement false. When coming to the second statement, it is true mainly because, in a boosted tree, that is the method that is applied to improve the overall performance of the model. The correct option will be B, i.e., only statement number two is TRUE, and statement number one is FALSE.

FYI: Free nlp course!

Q3. You will see four statements listed below. You will have to read all of them carefully and then choose one of the options from the ones that follow the four statements. The contextual question is, choose the statements that are true about Radom forests and the gradient boosting ensemble method.

1. Both Random Forest and Gradient Boosting ensemble methods can be used to perform classification.
2. Random Forests can be used to perform classification tasks, whereas the gradient boosting method can only perform regression.
3. Gradient boosting can be used to perform classification tasks, whereas the Random Forest method can only perform regression.
4. Both Random Forest and Gradient Boosting ensemble methods can be used to perform regression.
5. Only statement number one is TRUE.
6. Only statement number two is TRUE.
7. Both statements one and two are TRUE.
8. Only statement number three is TRUE
9. Only statement number four is TRUE
10. Only statements number one and four are TRUE

Ans. The answer to this question is straightforward. Both of these ensemble methods are actually very capable of doing both classification and regression tasks. So, the answer to this question would be F because only statements number one and four are TRUE.

Q4 You will see four statements listed below. You will have to read all of them carefully and then choose one of the options from the ones that follow the four statements. The contextual question is, consider a random forest of trees. So, what will be true about each of the trees in the random forest?

1. Each tree that constitutes the random forest is based on a subset of all the features.
2. Each of the trees in a random forest is built on all the features.
3. Each of the trees in a random forest is built on a subset of all the observations present.
4. Each of the trees in a random forest is built on the full observation set.
5. Only statement number one is TRUE.
6. Only statement number two is TRUE.
7. Both statements one and two are TRUE.
8. Only statement number three is TRUE
9. Only statement number four is TRUE
10. Both statements, number one and four, are TRUE
11. Both statements, number one and three are TRUE
12. Both statements number two and three are TRUE
13. Both statements number two and four are TRUE

Ans. The generation of random forests is based on the concept of bagging. To build a random forest, a small subset is taken from both the observations and the features. The values that are obtained after taking out the subsets are then fed into singular decision trees. Then all the values from all such decision trees are collected to make the final decision. That means the only statements that are correct would be one and three. So, the right option would be G.

Q5 You will see four statements listed below. You will have to read all of them carefully and then choose one of the options from the ones that follow the four statements. The contextual question is, select the correct statements about the hyperparameter known as “max_depth” of the gradient boosting algorithm.

1. Choosing a lower value of this hyperparameter is better if the validation set’s accuracy is similar.
2. Choosing a higher value of this hyperparameter is better if the validation set’s accuracy is similar.
3. If we are to increase this hyperparameter’s value, then the chances of this model actually overfitting the data increase.
4. If we are to increase this hyperparameter’s value, then the chances of this model actually underfitting the data increase.
5. Only statement number one is TRUE.
6. Only statement number two is TRUE.
7. Both statements one and two are TRUE.
8. Only statement number three is TRUE
9. Only statement number four is TRUE
10. Both statements number one and four are TRUE
11. Both the statements number one and three are TRUE
12. Both the statements number two and three are TRUE
13. Both the statements number two and four are TRUE

Ans. The hyperparameter max_depth controls the depth until the gradient boosting will model the presented data in front of it. If you keep on increasing the value of this hyperparameter, then the model is bound to overfit. So, statement number three is correct. If we have the same scores on the validation data, we generally prefer the model with a lower depth. So, statements number one and three are correct, and thus the answer to this decision tree interview question is g.

Q6. You will see four statements listed below. You will have to read all of them carefully and then choose one of the options from the options that follow the four statements. The contextual question is which of the following methods does not have a learning rate as one of its tunable hyperparameters?

1. Extra Trees.
2. AdaBoost
3. Random Forest
4. Gradient boosting.
5. Only statement number one is TRUE.
6. Only statement number two is TRUE.
7. Both statements one and two are TRUE.
8. Only statement number three is TRUE
9. Only statement number four is TRUE
10. Both statements number one and four are TRUE
11. Both the statements number one and three are TRUE
12. Both the statements number two and three are TRUE
13. Both the statements number two and four are TRUE

Ans. Only Extra Trees and Random forest does not have a learning rate as one of their tunable hyperparameters. So, the answer would be g because the statement number one and three are TRUE.

Q7. Choose the option, which is true.

1. Only in the algorithm of random forest, real values can be handled by making them discrete.
2. Only in the algorithm of gradient boosting, real values can be handled by making them discrete.
3. In both random forest and gradient boosting, real values can be handled by making them discrete.
4. None of the options which are mentioned above.

Ans. Both of the algorithms are capable ones. They both can easily handle the features which have real values in them. So, the answer to this decision tree interview questions and answers is C. 

Q8. Choose one option from the list below. The question is, choose the algorithm which is not an ensemble learning algorithm. 

1. Gradient boosting 
2. AdaBoost
3. Extra Trees
4. Random Forest
5. Decision Trees

Ans. This question is straightforward. Only one of these algorithms is not an ensemble learning algorithm. One thumb rule to keep in mind will be that any ensemble learning method would involve the use of more than one decision tree. Since in option E, there is just the singular decision tree, then that is not an ensemble learning algorithm. So, the answer to this question would be E (decision trees).

Q9. You will see two statements listed below. You will have to read both of them carefully and then choose one of the options from the two statements’ options. The contextual question is, which of the following would be true in the paradigm of ensemble learning.

1. The tree count in the ensemble should be as high as possible.
2. You will still be able to interpret what is happening even after you implement the algorithm of Random Forest.
3. Only statement number one is TRUE.
4. Only statement number two is TRUE.
5. Both statements one and two are TRUE.
6. None of the options which are mentioned above.

Ans. Since any ensemble learning method is based on coupling a colossal number of decision trees (which on its own is a very weak learner) together so it will always be beneficial to have more number of trees to make your ensemble method. However, the algorithm of random forest is like a black box. You will not know what is happening inside the model. So, you are bound to lose all the interpretability after you apply the random forest algorithm. So, the correct answer to this question would be A because only the statement that is true is the statement number one.

Best Machine Learning and AI Courses Online

Q10. Answer in only in TRUE or FALSE. Algorithm of bagging works best for the models which have high variance and low bias?

Ans. True. Bagging indeed is most favorable to be used for high variance and low bias model. 

Q11. . You will see two statements listed below. You will have to read both of them carefully and then choose one of the options from the two statements’ options. The contextual question is, choose the right ideas for Gradient boosting trees.

1. In every stage of boosting, the algorithm introduces another tree to ensure all the current model issues are compensated.
2. We can apply a gradient descent algorithm to minimize the loss function.
3. Only statement number one is TRUE.
4. Only statement number two is TRUE.
5. Both statements one and two are TRUE.
6. None of the options which are mentioned above.

Ans. The answer to this question is C meaning both of the two options are TRUE. For the first statement, that is how the boosting algorithm works. The new trees introduced into the model are just to augment the existing algorithm’s performance. Yes, the gradient descent algorithm is the function that is applied to reduce the loss function. 

Q12. In the gradient boosting algorithm, which of the statements below are correct about the learning rate?

1. The learning rate which you set should be as high as possible.
2. The learning rate which you set should not be as high as possible rather as low as you can make it.
3. The learning rate should be low but not very low.
4. The learning rate which you are setting should be high but not super high.

Check out: Machine Learning Interview Questions

Ans. The learning rate should be low, but not very low, so the answer to this decision tree interview questions and answers would be option C. 

Popular AI and ML Blogs & Free Courses

Apart from these brainstorming interview questions on decision tree, below are some basic decision tree interview questions that you could also give a look at. 

1. Explain what a decision tree algorithm is?

Ans. Decision tree algorithms can be explained as supervised learning algorithms that are majorly used in solving classification and regression problem statements. It functions by devising the larger dataset into smaller subsets and associating them with a decision tree simultaneously.  

The final result of the methodology is a decision tree with decision nodes and leaf nodes Any decision tree can operate on both numerical and categorical data. 

2. What are some of the most popular algorithms for deriving decision trees?

Ans. Some of the most popular algorithms used for curating decision trees include

• CART (Classification and Regression Trees)
• ID3 (Iterative Dichotomiser)
• C4.5 (Successor of ID3)

3. Elaborate on the concept of the CART algorithm for decision trees.

Ans. CART or Classification and Regression Trees is an algorithm that helps search at the top level by searching for an optimum split. It continues by repeating the same process at every subsequent level. 

At the same time, it also keeps verifying whether or not that split will lead to the lowest impurity. However, the solutions that this algorithm provides can not always be guaranteed to be optimal, yet it often provides solutions that are best suited. The reason behind it is that NP-Complete problems require exponential time complexity.

This helps make the problems more solvable even if they are in small training sets. This is the reason why opting for a best-fitting solution is better than looking for an optimal solution. 

4. Explain the structure of a decision tree.

Ans. A decision tree is a flowchart-like structure consisting of multiple components. It has parts named internal nodes, branches, leaf nodes and paths. Each carries a unique attribute. Internal nodes represent the test of a feature such as the outcomes of a dice roll, branch represents the outcomes of the test, leaf nodes are used as class labels and paths help form the classification rules starting from root to leaf. 

5. Mention the benefits of using decision trees.

Ans. The main advantage of using decision trees is that it is very simple to understand and explain at the same time. The best part is its ability to get visualized. A minute amount of data preprocessing is required yet it can handle both numerical and categorical data. Adding to that, it can also handle multiple output problems. 

Here are the benefits of using decision trees:

1. Interpretability: Decision trees offer a transparent and easy-to-understand model representation. The visual tree-like structure allows users to interpret and explain the decision-making process, making it ideal for both technical and non-technical stakeholders.
2. Feature Importance: Decision trees provide insights into the importance of different features in the data. By analyzing the splits and nodes, we can identify which variables have the most significant impact on the target variable, aiding in feature selection and data understanding.
3. Non-linear Relationships: Decision trees can handle non-linear relationships between variables, making them suitable for datasets with complex interactions. They can capture intricate patterns and interactions that linear models might miss.
4. Handling Missing Data: Decision trees can handle missing data without requiring imputation. When making predictions, the algorithm simply follows the available branches in the tree, making it robust to missing values.
5. Scalability: Decision trees can efficiently handle large datasets with minimal data preprocessing. They require relatively low computational resources compared to some other complex machine learning algorithms.
6. Multi-output Problems: Decision trees can be extended to address multi-output problems, allowing them to handle multiple target variables simultaneously.
7. Outlier Robustness: Decision trees are less affected by outliers compared to linear models. The hierarchical structure allows the algorithm to split data into regions, reducing the impact of extreme values.
8. Ensemble Methods: Decision trees can be combined using ensemble methods like Random Forests and Gradient Boosting, further improving predictive performance and generalization.
9. No Assumptions: Decision trees do not require the data to meet specific assumptions, making them more flexible and versatile for a wide range of datasets.
10. Applicability to Both Classification and Regression: Decision trees can be used for both classification and regression tasks, making them versatile tools in machine learning.

6. State the relation between Random Forest and Decision Trees.

Ans. Random Forest falls under ensemble learning methods, which is a machine learning method where several base models are combined to produce one optimal predictive model. In the case of Random Forest, those base models are decision trees, hence, it combines a number of decision trees in order to make the optimal prediction. A Random Forest can be curated for solving both classification and regression problems. 

Random Forest and Decision Trees are closely related in the field of machine learning, with Random Forest being an extension of the Decision Trees algorithm.

Decision Trees are a popular supervised learning algorithm used for both classification and regression tasks. They work by recursively splitting the data into subsets based on features to create a tree-like structure, where each node represents a decision based on specific feature values. The leaves of the tree correspond to the final decision or prediction.

Random Forest, on the other hand, is an ensemble learning method that builds multiple decision trees and combines their predictions to make a final decision. It introduces an element of randomness by using a technique called bootstrapping to create different subsets of the data for training each tree. Additionally, at each node, only a random subset of features is considered for splitting, which further adds diversity to the trees.

The relation between Random Forest and Decision Trees lies in their interdependence. Random Forest leverages the strength of Decision Trees while mitigating their weaknesses. Decision Trees are susceptible to overfitting, meaning they can learn the training data too well and perform poorly on new data. Random Forest addresses this issue by aggregating the predictions from multiple trees, reducing the risk of overfitting and improving the overall accuracy and robustness of the model.

7. What are the benefits of using Random Forest over Decision Trees?

Ans. The first and foremost reason for choosing Random Forest over Decision Trees is its ability to outperform the latter. Random Forest combines multiple Decision Trees, hence giving the optimal output, yet it does not overfit the data as Decision Trees often do. The reason is the nature of the training that Decision Trees have. They are trained on a very specific dataset, which results in overfitting. In the case of Random Forest, Decision Trees with different training sets can be accumulated together with the goal of decreasing the variance, therefore giving better outputs. 

8. When can a node be considered Pure?

Ans. A node is considered pure when all the data points within it belong to the same class. To determine purity, we use the Gini Index, a metric that measures how mixed the data is within a node. If the Gini Index of a node is zero (θ = 0), it means all elements belong to a single class, making the node pure.

To learn more about how the Gini Index helps in decision tree classification, check out the Gini Index formula and its role in calculating data impurity.

9. How are the different nodes represented in a diagram?

Ans. There are three types of nodes that make up a decision tree, and each uses different symbols. Decision nodes are characterized as squares and rectangles, Chance nodes are characterized by circles, and End nodes are characterized by triangles. Decision nodes are the points where a flow splits into multiple optional branches. Chance nodes are used for depicting the probability of certain results, and End nodes exhibit the final outcomes of the decision path. 

Hope these questions will add more value to your very own decision tree questions and answer PDF, and make you fully prepared for your dream job interview. Also, don’t forget to glance at your decision tree questions and answers pdf to revise concepts before the D-Day!

In-demand Machine Learning Skills

What Next?

If you’re interested to learn more about the decision tree, Machine Learning, check out IIIT-B & upGrad’s PG Diploma in Machine Learning & AI which is designed for working professionals and offers 450+ hours of rigorous training, 30+ case studies & assignments, IIIT-B Alumni status, 5+ practical hands-on capstone projects & job assistance with top firms.

Popular AI and ML Blogs & Free Courses