Ensemble Learning MCQs
1. Ensemble learning is a machine learning technique that involves:
a. Training multiple models and combining their predictions
b. Training a single model on a large dataset
c. Training a model using a deep neural network
d. Training a model using reinforcement learning
Answer: a. Training multiple models and combining their predictions
2. Which of the following is an example of an ensemble learning algorithm?
a. Decision Tree
b. Support Vector Machine (SVM)
c. Random Forest
d. K-Nearest Neighbors (KNN)
Answer: c. Random Forest
3. Bagging is an ensemble technique that:
a. Combines predictions using a weighted average
b. Trains multiple models on different subsets of the data
c. Constructs an ensemble by iteratively updating weights
d. Uses a committee of experts to make predictions
Answer: b. Trains multiple models on different subsets of the data
4. Boosting is an ensemble technique that:
a. Combines predictions using a weighted average
b. Trains multiple models on different subsets of the data
c. Constructs an ensemble by iteratively updating weights
d. Uses a committee of experts to make predictions
Answer: c. Constructs an ensemble by iteratively updating weights
5. AdaBoost is an example of:
a. Bagging algorithm
b. Boosting algorithm
c. Randomized algorithm
d. Reinforcement learning algorithm
Answer: b. Boosting algorithm
6. Gradient Boosting is an ensemble technique that:
a. Combines predictions using a weighted average
b. Trains multiple models on different subsets of the data
c. Constructs an ensemble by iteratively updating weights
d. Uses a committee of experts to make predictions
Answer: c. Constructs an ensemble by iteratively updating weights
7. XGBoost is a popular implementation of:
a. Bagging algorithm
b. Boosting algorithm
c. Random Forest algorithm
d. K-Means clustering algorithm
Answer: b. Boosting algorithm
8. Stacking is an ensemble technique that:
a. Combines predictions using a weighted average
b. Trains multiple models on different subsets of the data
c. Constructs an ensemble by iteratively updating weights
d. Trains a meta-model to make predictions based on outputs of base models
Answer: d. Trains a meta-model to make predictions based on outputs of base models
9. Which ensemble learning algorithm uses bootstrapping and feature sampling?
a. Random Forest
b. AdaBoost
c. Gradient Boosting
d. Stacking
Answer: a. Random Forest
10. The purpose of using ensemble learning is to:
a. Reduce overfitting and improve generalization
b. Increase training time and complexity
c. Decrease the number of models required
d. Eliminate the need for labeled data
Answer: a. Reduce overfitting and improve generalization
11. Bagging algorithms are effective in:
a. Handling imbalanced datasets
b. Sequential data prediction
c. Clustering high-dimensional data
d. Text classification tasks
Answer: a. Handling imbalanced datasets
12. Which ensemble learning algorithm assigns weights to base models based on their performance?
a. AdaBoost
b. Random Forest
c. Gradient Boosting
d. Stacking
Answer: a. AdaBoost
13. Which ensemble learning algorithm uses a committee of experts to make predictions?
a. Bagging
b. Boosting
c. Random Forest
d. Stacking
Answer: c. Random Forest
14. Which ensemble learning algorithm is prone to overfitting if the base models are too complex?
a. Bagging
b. Boosting
c. Random Forest
d. Stacking
Answer: b. Boosting
15. Which ensemble learning algorithm can handle both regression and classification tasks?
a. Bagging
b. AdaBoost
c. Gradient Boosting
d. Stacking
Answer: c. Gradient Boosting
16. Ensemble learning algorithms are useful when:
a. The dataset is small and low-dimensional
b. The dataset is large and high-dimensional
c. The dataset is perfectly balanced
d. The dataset contains only categorical variables
Answer: b. The dataset is large and high-dimensional
17. Ensemble learning algorithms can improve model performance by:
a. Reducing bias
b. Reducing variance
c. Increasing interpretability
d. Increasing training time
Answer: b. Reducing variance
18. Which ensemble learning algorithm can handle both numerical and categorical data without requiring one-hot encoding?
a. Bagging
b. AdaBoost
c. Gradient Boosting
d. Stacking
Answer: c. Gradient Boosting
19. Which ensemble learning algorithm is less sensitive to outliers?
a. Bagging
b. Boosting
c. Random Forest
d. Stacking
Answer: c. Random Forest
20. The majority voting method in ensemble learning refers to:
a. Combining predictions by averaging their probabilities
b. Combining predictions by taking the mode of their classes
c. Combining predictions by multiplying their probabilities
d. Combining predictions by taking the median of their values
Answer: b. Combining predictions by taking the mode of their classes
21. Which ensemble learning algorithm can handle missing values in the dataset?
a. Bagging
b. AdaBoost
c. Gradient Boosting
d. Stacking
Answer: a. Bagging
22. Ensemble learning algorithms are useful for:
a. Improving model stability
b. Increasing model complexity
c. Reducing feature importance
d. Eliminating the need for cross-validation
Answer: a. Improving model stability
23. Which ensemble learning algorithm can handle non-linear relationships in the data?
a. Bagging
b. AdaBoost
c. Gradient Boosting
d. Stacking
Answer: c. Gradient Boosting
24. Ensemble learning algorithms are effective in:
a. Reducing model interpretability
b. Increasing model training time
c. Handling unbalanced datasets
d. Eliminating the need for hyperparameter tuning
Answer: c. Handling unbalanced datasets
25. Which ensemble learning algorithm can handle both numerical and categorical features effectively?
a. Bagging
b. AdaBoost
c. Gradient Boosting
d. Stacking
Answer: a. Bagging
26. Which ensemble learning algorithm is less susceptible to overfitting compared to others?
a. Bagging
b. Boosting
c. Random Forest
d. Stacking
Answer: c. Random Forest
27. Which ensemble learning algorithm uses a weighted sum of predictions from base models?
a. Bagging
b. AdaBoost
c. Gradient
Boosting
d. Stacking
Answer: b. AdaBoost
28. Which ensemble learning algorithm can be used to identify important features in a dataset?
a. Bagging
b. AdaBoost
c. Gradient Boosting
d. Stacking
Answer: c. Gradient Boosting
29. Ensemble learning algorithms can be computationally expensive when:
a. The dataset is small
b. The base models are simple
c. The ensemble size is small
d. The dataset is large
Answer: d. The dataset is large
30. Which ensemble learning algorithm can be applied to both regression and classification tasks?
a. Bagging
b. AdaBoost
c. Random Forest
d. Stacking
Answer: c. Random Forest