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

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.

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?

Q4 | Which of the following is NOT supervised learning?

Q5 | How can you avoid overfitting ?

Q6 | What are the popular algorithms of Machine Learning?

Q7 | What is Training set?

Q8 | Common deep learning applications include        

Q9 | what is the function of Supervised Learning?

Q10 | Commons unsupervised applications include

Q11 | Reinforcement learning is particularly efficient when                         .

Q12 | if there is only a discrete number of possible outcomes (called categories), the process becomes a           .

Q13 | Which of the following are supervised learning applications

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.

Q15 | Which of the following sentence is correct?

Q16 | What is Test set?

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

Q18 | How it's possible to use a different placeholder through the parameter             .

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              .

Q20 | scikit-learn also provides a class for per- sample normalization, Normalizer. It can apply              to each element of a dataset

Q21 | There are also many univariate methods that can be used in order to select the best features according to specific criteria based on              .

Q22 | Which of the following selects only a subset of features belonging to a certain percentile

Q23 |               performs a PCA with non-linearly separable data sets.