XGBoost (Part 1): Machine Learning Interview Prep 16
Xtreme Gradient Boosting, XGBoost, is like a wizard in the realm of computer learning. It’s like a clever craftsman weaving together tiny threads of information into a magical tapestry of predictions. With every turn, XGBoost hones its skills like a diligent student, getting better and smarter at its task. It’s not just smart, it’s like having a super-smart friend who always helps you figure things out with ease.
Let’s check your basic knowledge of XGBoost. Here are 10 multiple-choice questions for you and there’s no time limit. Have fun!
1. Which of the following is a feature of XGBoost?
(A) Linear scalability
(B) Handling of missing data
(C) Only supports regression
(D) Cannot process categorical data
2. DMatrix is a data structure unique to XGBoost that optimizes both memory efficiency and training speed. What does the term ‘DMatrix’ refer to?
(A) Data Matrix
(B) Decision Matrix
(C) Distributed Matrix
(D) Dimension Matrix
3. What is the primary objective of XGBoost?
(A) Binary Classification
(B) Multi-class Classification
(C) Regression
(D) All of the above
4. Early stopping refers to the technique of stopping the training process if the model’s performance doesn’t improve for a specified number of rounds. What does ‘early stopping’ mean in the context of XGBoost?
(A) Stopping the training process early to prevent overfitting
(B) Early prediction of outcomes
(C) Early initialization of weights
(D) Stopping the use of boosting rounds
5. What is ‘boosting’ in XGBoost?
(A) Increasing the size of the dataset
(B) Merging multiple models into one
(C) Sequentially improving weak learners
(D) Enhancing the computation speed
6. How does XGBoost handle overfitting?
(A) By limiting the maximum depth of trees
(B) By increasing the learning rate
(C) By decreasing the number of trees
(D) By using a single decision tree
7. Which feature of XGBoost contributes to its high execution speed?
(A) Parallel processing of trees
(B) Sequential processing of trees
(C) Use of a single decision tree
(D) Smaller datasets
8. How does XGBoost perform feature selection?
(A) Through random selection
(B) Based on feature importance scores
(C) Using all features equally
(D) Through principal component analysis
9. Which of these is a key advantage of XGBoost over traditional gradient boosting?
(A) Slower execution
(B) High model interpretability
(C) Improved handling of missing data
(D) Inability to process categorical data
10. What is the difference between XGBoost and Random Forest? (Select two)
(A) XGBoost uses gradient boosting while Random Forest uses bagging
(B) XGBoost uses bagging while Random Forest uses gradient boosting
(C) XGBoost creates new models that focus on correcting the errors of the previous models, while Random Forest creates independent decision trees.
(D) XGBoost creates independent decision trees, while Random Forest creates new models that focus on correcting the errors of the previous models.
The solutions will be published in the next blog XGBoost (Part 2): Machine Learning Interview Prep 17.
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The solution of Principal Component Analysis (PCA) Part 2: Machine Learning Interview Prep 15 - 1(C), 2(C), 3(A), 4(A), 5(C), 6(B), 7(B), 8(C), 9(A), 10(B).
References:
[1] XGBoost Part 1 (of 4): Regression
[2] XGBoost Part 2 (of 4): Classification
[3] XGBoost Part 3 (of 4): Mathematical Details
[4] XGBoost Part 4 (of 4): Crazy Cool Optimizations
[5] XGBoost in Python from Start to Finish
[6] Interview questions for XG Boost
[7] All about XGBoost
[8] Tell us something about XGBoost. Why is it so popular?
[9] ChatGPT to rewrite the questions simply.
