Below are the two ensemble models:
1. E1(M1, M2, M3) and
2. E2(M4, M5, M6)
Above, Mx is the individual base models.
Which of the following are more likely to choose if following conditions for E1 and E2 are given?
E1: Individual Models accuracies are high but models are of the same type or in another term less diverse
E2: Individual Models accuracies are high but they are of different types in another term high diverse in nature

Related MCQs

• Suppose, you want to apply a stepwise forward selection method for choosing the best models for an ensemble model. Which of the following is the correct order of the steps? Note: You have more than 1000 models predictions 1. Add the models predictions (or in another term take the average) one by one in the ensemble which improves the metrics in the validation set. 2. Start with empty ensemble 3. Return the ensemble from the nested set of ensembles that has maximum performance on the validation set
• Regarding bias and variance, which of the following statements are true? (Here ‘high’ and ‘low’ are relative to the ideal model. (i) Models which overfit are more likely to have high bias (ii) Models which overfit are more likely to have low bias (iii) Models which overfit are more likely to have high variance (iv) Models which overfit are more likely to have low variance
• Regarding bias and variance, which of the following statements are true? (Here ‘high’ and ‘low’ are relative to the ideal model. (i) Models which overfit are more likely to have high bias (ii) Models which overfit are more likely to have low bias (iii) Models which overfit are more likely to have high variance (iv) Models which overfit are more likely to have low variance
• If you use an ensemble of different base models, is it necessary to tune the hyper parameters of all base models to improve the ensemble performance?
• If you use an ensemble of different base models, is it necessary to tune the hyper parameters of all base models to improve the ensemble performance?
• 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 parameters 2. Different learners can come from different algorithms 3. Different learners can come from different training spaces
• Which of the following is / are true about weak learners used in ensemble model? 1. They have low variance and they don’t usually overfit 2. They have high bias, so they can not solve hard learning problems 3. They have high variance and they don’t usually overfit
• Suppose, you have 2000 different models with their predictions and want to ensemble predictions of best x models. Now, which of the following can be a possible method to select the best x models for an ensemble?
• 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.
• 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.