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Ensemble Modeling Interview Questions and Answers
Meet the Author : Mr. Bharani Kumar
Bharani Kumar Depuru is a well known IT personality from Hyderabad. He is the Founder and Director of Innodatatics Pvt Ltd and 360DigiTMG. Bharani Kumar is an IIT and ISB alumni with more than 17 years of experience, he held prominent positions in the IT elites like HSBC, ITC Infotech, Infosys, and Deloitte. He is a prevalent IT consultant specializing in Industrial Revolution 4.0 implementation, Data Analytics practice setup, Artificial Intelligence, Big Data Analytics, Industrial IoT, Business Intelligence and Business Management. Bharani Kumar is also the chief trainer at 360DigiTMG with more than Ten years of experience and has been making the IT transition journey easy for his students. 360DigiTMG is at the forefront of delivering quality education, thereby bridging the gap between academia and industry.
Table of Content
- How is the Decision Tree constructed?
- What are the properties of Decision Trees?
- How does Decision Tree regression output work?
- What is the use of Decision Tree regression?
- To do the decision tree in R we will use which library?
- This Decision Tree is called _________?(which decision tree)
- Bootstrap aggregation is known for___________?
- In boosting technique what does the base_estimator parameter do?
- Which one is not in the Ensemble Techniques algorithm?
- Bagging comes under which _________ method?
- Which of the following are features of Random Forest?
- In boosting technique what does the n_estimator parameter do?
- Choose the wrong statement for ensemble technique?
- Which of the following ensemble techniques doesn’t use learning Rate as one of its hyper parameters?
- What is the importance of using the learning rate to get optimum output in boosting technique? Which of the following is true about choosing the learning rate?
- In random forest which hyper parameters are used for increasing the model's speed? Which of the following is the true statement?
- In a random forest, which hyper parameters are used for increasing the predictive power? Which of the following is a true statement?
- Ensemble technique can be used for both Regression and Classification Tasks?
-
How is the Decision Tree constructed?
- a) Entropy.
- b) Information Gain.
- c) Gini Impurity.
- d) All the above.
Answer - d) All the above
-
What are the properties of Decision Trees?
- a) Low Bias and high variance.
- b) Low bias and low variance.
- c) High bias and high variance.
- d) High bias and low variance.
Answer - a) Low Bias and high variance
-
How does Decision Tree regression output work?
- a) Mean and median.
- b) Majority voting.
- c) All the above.
- d) None of this.
Answer - a) Mean and median
-
What is the use of Decision Tree regression?
- a) Entropy.
- b) Information Gain.
- c) Mean squared error.
- d) All the above.
Answer - d) All the above
-
To do the decision tree in R we will use which library?
- a) e1071.
- b) C5.0.
- c) dist.
- d) scale.
Answer - b) C5.0
-
This Decision Tree is called _________?(which decision tree)
- a) Greedy.
- b) Lazy Learner.
- c) Market Basket.
- d) Boosting.
Answer - a) Greedy
-
Bootstrap aggregation is known for___________?
- a) Combines multiple weak learners into a single strong learner.
- b) Sequential method.
- c) Row sampling with replacement.
- d) All the above.
Answer - c) Row sampling with replacement
-
In boosting technique what does the base_estimator parameter do?
- a) Weak learner used to train the model.
- b) Weak learners to train iteratively.
- c) Contributes to the weights of weak learners.
- d) All the above.
Answer - a) Weak learner used to train the model
-
Which one is not in the Ensemble Techniques algorithm?
- a) Random forest.
- b) Ad boost.
- c) GBM.
- d) Decision Tree
Answer - d) Decision Tree
-
Bagging comes under which _________ method?
- a) Sequential methods.
- b) Parallel method.
- c) Line method.
- d) Dot method.
Answer - b) Parallel method
-
Which of the following are features of Random Forest?
- a) Reduce the variance.
- b) Handle Over-Fitting.
- c) It can also have internally imputation technique.
- d) All of the above.
Answer - d) All of the above
-
In boosting technique what does the n_estimator parameter do?
- a) Weak learner used to train the model.
- b) Weak learners to train iteratively.
- c) Contributes to the weights of weak learners.
- d) All the above.
Answer - b) Weak learners to train iteratively
-
Choose the wrong statement for ensemble technique?
- a) Based on the voting we can choose final classification prediction.
- b) Based on the mean and median we can choose final regression prediction.
- c) Avoid over–fitting.
- d) Greedy algorithm is also known as ensemble technique.
Answer - d) Greedy algorithm is also known as ensemble technique
-
Which of the following ensemble techniques doesn’t use learning Rate as one of its hyper parameters?
- a) Gradient Boosting
- b) XGBMs
- c) AdaBoost
- d) Random Forest
Answer - d) Random Forest
-
What is the importance of using the learning rate to get optimum output in boosting technique? Which of the following is true about choosing the learning rate?
- a) Learning rate should be as high as possible.
- b) Learning Rate should be as low as possible.
- c) Learning Rate should be low but it should not be very low.
- d) Learning rate should be high but it should not be very high.
Answer - c) Learning Rate should be low but it should not be very low
-
In random forest which hyper parameters are used for increasing the model's speed? Which of the following is the true statement?
Statement 1: n_jobs, random_state, oob_score(obb sampling)
Statement 2: n_estimators, max_features, min_sample_leaf- a) Statement 1 is true and statement 2 is false.
- b) Statement 1 is False and statement 2 is true.
- c) Both Statement (1 & 2) is wrong.
- d) Both Statement (1 & 2) is true.
Answer - a) Statement 1 is true and statement 2 is false
-
In a random forest, which hyperparameters are used for increasing the predictive power? Which of the following is a true statement?
Statement 1: n_jobs, random_state, oob_score(obb sampling)
Statement 2: n_estimators, max_features, min_sample_leaf- a) Statement 1 is true and statement 2 is false.
- b) Statement 1 is False and statement 2 is true.
- c) Both Statement (1 & 2) is wrong.
- d) Both Statement (1 & 2) is true.
Answer - b) Statement 1 is False and statement 2 is true
-
Ensemble technique can be used for both Regression and Classification Tasks?
- a) Possible Scenarios can be added.
- b) True.
- c) False.
- d) All the above.
Answer - b) True
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