Machine Learning Set 11

Q1 | True or False: Ensemble learning can only be applied to supervised learning methods.

Q2 | True or False: Ensembles will yield bad results when there is significant diversity among the models.Note: All individual models have meaningful and good predictions.

Q3 | 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

Q4 | True or False: Ensemble of classifiers may or may not be more accurate than any of its individual model.

Q5 | 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?

Q6 | Generally, an ensemble method works better, if the individual base models have ____________?Note: Suppose each individual base models have accuracy greater than 50%.

Q7 | 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.

Q8 | 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

Q9 | 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

Q10 | Which of the following parameters can be tuned for finding good ensemble model in bagging based algorithms?1. Max number of samples2. Max features3. Bootstrapping of samples4. Bootstrapping of features

Q11 | How is the model capacity affected with dropout rate (where model capacity means the ability of a neural network to approximate complex functions)?

Q12 | True or False: Dropout is computationally expensive technique w.r.t. bagging

Q13 | 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 predictions1. 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 ensemble3. Return the ensemble from the nested set of ensembles that has maximum performance on the validation set

Q14 | Below are the two ensemble models:1. E1(M1, M2, M3) and2. 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 diverseE2: Individual Models accuracies are high but they are of different types in another term high diverse in nature

Q15 | True or False: In boosting, individual base learners can be parallel.

Q16 | Which of the following is true about bagging?1. Bagging can be parallel2. The aim of bagging is to reduce bias not variance3. Bagging helps in reducing overfitting

Q17 | Suppose you are using stacking with n different machine learning algorithms with k folds on data.Which of the following is true about one level (m base models + 1 stacker) stacking?Note:Here, we are working on binary classification problemAll base models are trained on all featuresYou are using k folds for base models

Q18 | Which of the following is the difference between stacking and blending?

Q19 | Which of the following can be one of the steps in stacking?1. Divide the training data into k folds2. Train k models on each k-1 folds and get the out of fold predictions for remaining one fold3. Divide the test data set in “k” folds and get individual fold predictions by different algorithms

Q20 | Q25. Which of the following are advantages of stacking?1) More robust model2) better prediction3) Lower time of execution

Q21 | Which of the following are correct statement(s) about stacking?A machine learning model is trained on predictions of multiple machine learning modelsA Logistic regression will definitely work better in the second stage as compared to other classification methodsFirst stage models are trained on full / partial feature space of training data

Q22 | Which of the following is true about weighted majority votes?1. We want to give higher weights to better performing models2. Inferior models can overrule the best model if collective weighted votes for inferior models is higher than best model3. Voting is special case of weighted voting

Q23 | Which of the following is true about averaging ensemble?

Q24 | How can we assign the weights to output of different models in an ensemble?1. Use an algorithm to return the optimal weights2. Choose the weights using cross validation3. Give high weights to more accurate models