gs gs126 Ensemble Learning - Ensemble Learning – Model Combination Schemes - Quiz No.1
gs gs126 Neural Networks Quiz
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Question 1: Which of the following statements is false about Ensemble learning?
It is a supervised learning algorithm
More random algorithms can be used to produce a stronger ensemble
It is an unsupervised learning algorithm
Ensembles can be shown to have more flexibility in the functions they can represent
Question 3: Which of the following is not a multi – expert model combination scheme to generate the final output?
Global approach
Local approach
Parallel approach
Serial approach
Question 5: Which of the following statements is true about multi – stage combination methods?
The next base – learner is trained on only the instances where the previous base – learners are not accurate enough
It is a selection approach
It has base – learners that work in parallel
The base – learners are sorted in decreasing complexity
Question 6: Which of the following is not an example of a multi – expert combination method?
Voting
Stacking
Mixture of experts
Cascading
Question 7: Which of the following statements is false about the base – learners?
The base – learners are chosen for their accuracy
The base – learners are chosen for their simplicity
The base – learners has to be diverse
Base – learners do not require them to be very accurate individually
Question 11: Which of the following is represented by the below figure?
Stacking
Mixture of Experts
Bagging
Boosting
Question 14: Consider there are 5 employees A, B, C, D, and E of ABC company. Where people A, B and C are experienced, D and E are fresher. They have rated the company app as given in the table. What will be the final prediction if we are taking the weighted average?
| Employee | Weight | Rating |
| A | 0.4 | 3 |
| B | 0.4 | 2 |
| C | 0.4 | 2 |
| D | 0.2 | 2 |
| E | 0.2 | 4 |
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3
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