True or False: Ensemble of classifiers may or may not be more accurate than any of its individual model.

Related MCQs

• Suppose there are 25 base classifiers. Each classifier has error rates of e = 0.35. Suppose you are using averaging as ensemble technique. What will be the probabilities that ensemble of above 25 classifiers will make a wrong prediction? Note: All classifiers are independent of each other
• 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
• What is true about an ensembled classifier? 1. Classifiers that are more “sure” can vote with more conviction 2. Classifiers can be more “sure” about a particular part of the space 3. Most of the times, it performs better than a single classifier
• In machine learning, an algorithm (or learning algorithm) is said to be unstable if a small change in training data cause the large change in the learned classifiers. True or False: Bagging of unstable classifiers is a good idea
• 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
• 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?
• 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.
• 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.
• 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?