ML with python

What is the primary goal of ensemble learning?

• Increase model complexity

• Reduce bias and variance
  

• Reduce computational cost
  

• Increase dataset size

Which ensemble technique creates multiple subsets of the training data by resampling with replacement?

• Bagging

• Boosting

• Stacking

• KNN

Which of the following is NOT an ensemble method?

• Bagging

• Boosting

• Stacking

• K-means

Which technique focuses on training weak models sequentially, where each model corrects errors of the previous one?

• Bagging

• Boosting

• Stacking

• Voting

In Stacking, how are the predictions of base models combined?

• Averaging

• Weighted majority voting

• Using a meta-learner

• Random selection

Which ensemble method assigns different weights to weak learners based on their accuracy?

• Bagging

• Boosting

• Stacking

• Random Forest

Which of the following is an example of a boosting algorithm?

• Random Forest

• AdaBoost

• KNN

• K-means

Random Forest is an example of which ensemble technique?

• Bagging

• Boosting

• Stacking

• K-means

What is the key difference between hard voting and soft voting in ensemble learning?

• Hard voting considers only probabilities, while soft voting uses majority class

• Hard voting uses majority class predictions, while soft voting considers probability scores

• Soft voting applies only to regression problems

• Hard voting requires fewer models than soft voting

Which of the following is a key characteristic of unsupervised learning?

• Requires labeled data

• Uses supervised feedback

• Learns patterns from unlabeled data

• Requires a target variable