On This Page

This set of Machine Learning (ML) Multiple Choice Questions & Answers (MCQs) focuses on Machine Learning Set 12

Q1 | 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 problem1. Median2. Product3. Average4. Weighted sum5. Minimum and Maximum6. Generalized mean rule
  • 1, 3 and 4
  • 1,3 and 6
  • 1,3, 4 and 6
  • all of above
Q2 | 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.
  • bagging
  • 1,3 and 6
  • a or b
  • none of these
Q3 | 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?
  • yes
  • no
  • can’t say
Q4 | Which of the following is NOT supervised learning?
  • pca
  • decision tree
  • linear regression
  • naive bayesian
Q5 | How can you avoid overfitting ?
  • by using a lot of data
  • by using inductive machine learning
  • by using validation only
  • none of above
Q6 | What are the popular algorithms of Machine Learning?
  • decision trees and neural networks (back propagation)
  • probabilistic networks and nearest neighbor
  • support vector machines
  • all
Q7 | What is Training set?
  • training set is used to test the accuracy of the hypotheses generated by the learner.
  • a set of data is used to discover the potentially predictive relationship.
  • both a & b
  • none of above
Q8 | Common deep learning applications include        
  • image classification, real-time visual tracking
  • autonomous car driving, logistic optimization
  • bioinformatics, speech recognition
  • all above
Q9 | what is the function of Supervised Learning?
  • classifications, predict time series, annotate strings
  • speech recognition, regression
  • both a & b
  • none of above
Q10 | Commons unsupervised applications include
  • object segmentation
  • similarity detection
  • automatic labeling
  • all above
Q11 | Reinforcement learning is particularly efficient when                         .
  • the environment is not completely deterministic
  • it\s often very dynamic
  • it\s impossible to have a precise error measure
  • all above
Q12 | if there is only a discrete number of possible outcomes (called categories), the process becomes a           .
  • regression
  • classification.
  • modelfree
  • categories
Q13 | Which of the following are supervised learning applications
  • spam detection, pattern detection, natural language processing
  • image classification, real-time visual tracking
  • autonomous car driving, logistic optimization
  • bioinformatics, speech recognition
Q14 | 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.
  • logical
  • classical
  • classification
  • none of above
Q15 | Which of the following sentence is correct?
  • machine learning relates with the study, design and
  • data mining can be defined as the process in which the
  • both a & b
  • none of the above
Q16 | What is Test set?
  • test set is used to test the accuracy of the hypotheses generated by the learner.
  • it is a set of data is used to discover the potentially predictive relationship.
  • both a & b
  • none of above
Q17 |               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
  • removing the whole line
  • creating sub-model to predict those features
  • using an automatic strategy to input them according to the other known values
  • all above
Q18 | How it's possible to use a different placeholder through the parameter             .
  • regression
  • classification
  • random_state
  • missing_values
Q19 | 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              .
  • robustscaler
  • dictvectorizer
  • labelbinarizer
  • featurehasher
Q20 | scikit-learn also provides a class for per- sample normalization, Normalizer. It can apply              to each element of a dataset
  • max, l0 and l1 norms
  • max, l1 and l2 norms
  • max, l2 and l3 norms
  • max, l3 and l4 norms
Q21 | There are also many univariate methods that can be used in order to select the best features according to specific criteria based on              .
  • f-tests and p-values
  • chi-square
  • anova
  • all above
Q22 | Which of the following selects only a subset of features belonging to a certain percentile
  • selectpercentile
  • featurehasher
  • selectkbest
  • all above
Q23 |               performs a PCA with non-linearly separable data sets.
  • sparsepca
  • kernelpca
  • svd
  • none of the mentioned