Machine Learning (ML) MCQ Quiz Hub

Machine Learning (ML) MCQ Set 05

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1. 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




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




3. 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




4. 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




5. 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




6. 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 problem All base models are trained on all features You are using k folds for base models




7. Which of the following is the difference between stacking and blending?




8. Which of the following can be one of the steps in stacking? 1. Divide the training data into k folds 2. Train k models on each k-1 folds and get the out of fold predictions for remaining one fold 3. Divide the test data set in “k” folds and get individual fold predictions by different algorithms




9. Q25. Which of the following are advantages of stacking? 1) More robust model 2) better prediction 3) Lower time of execution




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




11. Which of the following is true about weighted majority votes? 1. We want to give higher weights to better performing models 2. Inferior models can overrule the best model if collective weighted votes for inferior models is higher than best model 3. Voting is special case of weighted voting




12. Which of the following is true about averaging ensemble?




13. How can we assign the weights to output of different models in an ensemble? 1. Use an algorithm to return the optimal weights 2. Choose the weights using cross validation 3. Give high weights to more accurate models




14. Suppose you are given ‘n’ predictions on test data by ‘n’ different models (M1, M2, …. Mn) respectively. Which of the following method(s) can be used to combine the predictions of these models? Note: We are working on a regression problem 1. Median 2. Product 3. Average 4. Weighted sum 5. Minimum and Maximum 6. Generalized mean rule




15. 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




16. Which of the following is NOT supervised learning?




17. How can you avoid overfitting ?




18. What are the popular algorithms of Machine Learning?




19. What is Training set?




20. Common deep learning applications include




21. what is the function of Supervised Learning?




22. Commons unsupervised applications include




23. Reinforcement learning is particularly efficient when




24. if there is only a discrete number of possible outcomes (called categories), the process becomes a




25. Which of the following are supervised learning applications




26. During the last few years, many algorithms have been applied to deep neural networks to learn the best policy for playing Atari video games and to teach an agent how to associate the right action with an input representing the state.




27. Which of the following sentence is correct?




28. What is Overfitting in Machine learning?




29. What is Test set?




30. is much more difficult because it's necessary to determine a supervised strategy to train a model for each feature and, finally, to predict their value




31. How it's possible to use a different placeholder through the parameter .




32. If you need a more powerful scaling feature, with a superior control on outliers and the possibility to select a quantile range, there's also the class




33. scikit-learn also provides a class for per- sample normalization, Normalizer. It can apply to each element of a dataset




34. There are also many univariate methods that can be used in order to select the best features according to specific criteria based on




35. Which of the following selects only a subset of features belonging to a certain percentile




36. performs a PCA with non-linearly separable data sets.




37. A feature F1 can take certain value: A, B, C, D, E, & F and represents grade of students from a college.Which of the following statement is true in following case?




38. What would you do in PCA to get the same projection as SVD?




39. What is PCA, KPCA and ICA used for?




40. What are common feature selection methods in regression task?




41. The parameter allows specifying the percentage of elements to put into the test/training set




42. In many classification problems, the target is made up of categorical labels which cannot immediately be processed by any algorithm.




43. adopts a dictionary-oriented approach, associating to each category label a progressive integer number.




44. If Linear regression model perfectly first i.e., train error is zero, then




45. Which of the following metrics can be used for evaluating regression models?i) R Squaredii) Adjusted R Squarediii) F Statisticsiv) RMSE / MSE / MAE




46. In a simple linear regression model (One independent variable), If we change the input variable by 1 unit. How much output variable will change?




47. Function used for linear regression in R is




48. In syntax of linear model lm(formula,data,..), data refers to




49. In the mathematical Equation of Linear Regression Y?=??1 + ?2X + ?, (?1, ?2) refers to




50. Which of the following methods do we use to find the best fit line for data in Linear Regression?