Top 350+ Solved Machine Learning (ML) MCQ Questions Answer
Q. Which of the following option is / are correct regarding benefits of ensemble model?1. Better performance2. Generalized models3. Better interpretability
a. 1 and 3
b. 2 and 3
c. 1 and 2
d. 1, 2 and 3
Q. Which of the following can be true for selecting base learners for an ensemble?1. Different learners can come from same algorithm with different hyper parameters2. Different learners can come from different algorithms3. Different learners can come from different training spaces
a. 1
b. 2
c. 1 and 3
d. 1, 2 and 3
Q. Which of the following is / are true about weak learners used in ensemble model?1. They have low variance and they don’t usually overfit2. They have high bias, so they can not solve hard learning problems3. They have high variance and they don’t usually overfit
a. 1 and 2
b. 1 and 3
c. 2 and 3
d. none of these
Q. Generally, an ensemble method works better, if the individual base models have ____________?Note: Suppose each individual base models have accuracy greater than 50%.
a. less correlation among predictions
b. high correlation among predictions
c. correlation does not have any impact on ensemble output
d. none of the above
Q. In an election, N candidates are competing against each other and people are voting for either of the candidates. Voters don’t communicate with each other while casting their votes. Which of the following ensemble method works similar to above-discussed election procedure?Hint: Persons are like base models of ensemble method.
a. bagging
b. boosting
c. a or b
d. none of these
Q. How is the model capacity affected with dropout rate (where model capacity means the ability of a neural network to approximate complex functions)?
a. model capacity increases in increase in dropout rate
b. model capacity decreases in increase in dropout rate
c. model capacity is not affected on increase in dropout rate
d. none of these