Machine Learning (ML) MCQs | Page - 26

Q. 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

Q. 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

Q. Which of the following parameters can be tuned for finding good ensemble model in bagging based algorithms?
1. Max number of samples
2. Max features
3. Bootstrapping of samples
4. Bootstrapping of features

Q. How is the model capacity affected with dropout rate (where model capacity means the ability of a neural network to approximate complex functions)?

Q. True or False: Dropout is computationally expensive technique w.r.t. bagging

Q. 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

Q. 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?

Q. 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

Q. True or False: In boosting, individual base learners can be parallel.

Q. Which of the following is true about bagging?
1. Bagging can be parallel
2. The aim of bagging is to reduce bias not variance
3. Bagging helps in reducing overfitting