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.

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. Try to solve each of these questions first before reading the solutions to gain the most out of these questions.

Table of Contents

**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’ options. The contextual question is, Choose the statements which are true about bagging trees.**

- The individual trees are not at all dependent on each other for a bagging tree.
- To improve the overall performance of the model, the aggregate is taken from weak learners. This method is known as bagging trees.
- Only statement number one is TRUE.
- Only statement number two is TRUE.
- Both statements one and two are TRUE.
- 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’ options. The contextual question is, Choose the statements which are true about boosting trees.**

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

**Ans. **If you were to understand how the boosting of trees is done, you will understand and will be able to differentiate the correct statement from the statement, which 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 which 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 the statement number two is TRUE, and the statement number one is FALSE.

**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 options which follows the four statements. The contextual question is, Choose the statements which are true about Radom forests and Gradient boosting ensemble method.**

- Both Random forest and Gradient boosting ensemble methods can be used to perform classification.
- Random Forests can be used to perform classification tasks, whereas the gradient boosting method can only perform regression.
- Gradient boosting can be used to perform classification tasks, whereas the Random Forest method can only perform regression.
- Both Random forest and Gradient boosting ensemble methods can be used to perform regression.
- Only statement number one is TRUE.
- Only statement number two is TRUE.
- Both statements one and two are TRUE.
- Only statement number three is TRUE
- Only statement number four is TRUE
- Only statement number one and four is 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 options which follows the four statements. The contextual question is, consider a random forest of trees. So what will be true about each or any of the trees in the random forest?**

- Each tree which constitutes the random forest is based on the subset of all the features.
- Each of the in a random forest is built on all the features.
- Each of the trees in a random forest is built on a subset of all the observations present.
- Each of the trees in a random forest is built on the full observation set.
- Only statement number one is TRUE.
- Only statement number two is TRUE.
- Both statements one and two are TRUE.
- Only statement number three is TRUE
- Only statement number four is TRUE
- Both statements number one and four are TRUE
- Both the statements number one and three are TRUE
- Both the statements number two and three are TRUE
- Both the 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 which 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 which 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 options which follows the four statements. The contextual question is, select the correct statements about the hyperparameter known as “max_depth” of the gradient boosting algorithm.**

- Choosing a lower value of this hyperparameter is better if the validation set’s accuracy is similar.
- Choosing a higher value of this hyperparameter is better if the validation set’s accuracy is similar.
- If we are to increase this hyperparameter’s value, then the chances of this model actually overfitting the data increases.
- If we are to increase this hyperparameter’s value, then the chances of this model actually underfitting the data increases.
- Only statement number one is TRUE.
- Only statement number two is TRUE.
- Both statements one and two are TRUE.
- Only statement number three is TRUE
- Only statement number four is TRUE
- Both statements number one and four are TRUE
- Both the statements number one and three are TRUE
- Both the statements number two and three are TRUE
- 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 questions 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 which follows the four statements. The contextual question is which of the following methods does not have a learning rate as one of their tunable hyperparameters.**

- Extra Trees.
- AdaBoost
- Random Forest
- Gradient boosting.
- Only statement number one is TRUE.
- Only statement number two is TRUE.
- Both statements one and two are TRUE.
- Only statement number three is TRUE
- Only statement number four is TRUE
- Both statements number one and four are TRUE
- Both the statements number one and three are TRUE
- Both the statements number two and three are TRUE
- 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.**

**Only in the algorithm of random forest, real values can be handled by making them discrete.****Only in the algorithm of gradient boosting, real values can be handled by making them discrete.****In both random forest and gradient boosting, real values can be handled by making them discrete.****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. **

**Gradient boosting****AdaBoost****Extra Trees****Random Forest****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.**

- The tree count in the ensemble should be as high as possible.
- You will still be able to interpret what is happening even after you implement the algorithm of Random Forest.
- Only statement number one is TRUE.
- Only statement number two is TRUE.
- Both statements one and two are TRUE.
- 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.

**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.**

- In every stage of boosting, the algorithm introduces another tree to ensure all the current model issues are compensated.
- We can apply a gradient descent algorithm to minimize the loss function.
- Only statement number one is TRUE.
- Only statement number two is TRUE.
- Both statements one and two are TRUE.
- 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?**

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

**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.

**Check out: **Machine Learning Interview Questions

**What Next?**

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## How can the decision tree be improved?

A decision tree is A tool to create a simple visual aid in which conditional autonomous or decision points are represented as nodes and the various possible outcomes as leaves. In simple words, a decision tree is a model of the decision-making process. You can improve the decision tree by ensuring that the stop criteria is always explicit. When the stop criteria is not explicit it leaves one wondering if further exploration is necessary, and also leaves doubts about whether one should stop or not. The decision tree should also be constructed in such a way that it becomes easy to follow and not confuse the reader.

## Why is decision tree accuracy so low?

Decision tree accuracy is lower than what we would have expected. This can happen due to the following reasons: Bad data - It is very important to use the correct data for machine learning algorithms. Bad data can lead to wrong results. Randomness - Sometimes, the system is so complex that it is impossible to predict what will happen in future. In this case, the accuracy of the decision tree will drop as well. Overfitting - The decision tree may not be able to capture the uniqueness of the data, and so it can be considered as a generalization. If the same data is used to adjust the tree, it can over-fit the data.

## How is a decision tree pruned?

A decision tree is pruned using a branch and bound algorithm. A branch and bound algorithm finds the optimal solution to the decision tree by iterating through the nodes of the tree and bounding the value of the objective function at each iteration. The objective function is the value of the decision tree to the business. At each node, the algorithm either removes a branch of the tree or prunes a branch to a new node. The best part is that a branch can be pruned even if it leads to a non-optimal solution.