## Which one of the following functions returns the bottom five rows of the dataset ‘Mobile’?

1. What is the correct output of the command is.na( c(4,5,NA))?

(a) FALSE FALSE TRUE (b) FALSE TRUE TRUE

(c) FALSE TRUE FALSE (d) TRUE FALSE TRUE

2. Which one of the following functions displays the variables of the given dataset?

(a) summary() (b) names()

(c) str() (d) install()

3. Which one of the following functions displays the structure of the given dataset?

(a) summary() (b) names()

(c) str() (d) install()

4. Which one of the following functions returns the number of categorical value after counting

it?

(a) table(dataset\$variablenames) (b) table(dataset.variablenames)

(c) table(dataset) (d) table(variablenames)

5. How many rows are returned by the head() or tail() function by default?

(a) 1 (b) 4

(c) 6 (d) 5

6. Which one of the following functions returns the bottom five rows of the dataset ‘Mobile’?

(c) tail(Mobile) (d) tail(Mobile,5)

### Compute the information gain of the term “elections” according to Eq. 5.7.

Consider the term “elections” which is present in only 50 documents in a corpus of 1000 documents. Furthermore, assume that the corpus contains 100 documents belonging to the Politics category,….

### Predict the probabilities of categories Cat and Car of Test2 on the toy corpus example in Sect. 5.3.5.2. You can use the multinomial na¨ıve Bayes model with the same level of smoothing as used in the example in the book. Return normalized probabilities that sum to 1 over the two categories. 2.       Na¨ıve Bayes is a generative model in which each class corresponds to one mixture component. Design a fully supervised generalization of the nai¨ıve Bayes model in which each of the k classes contains exactly b > 1 mixture components for a total of b · k mixture components. How would you perform parameter estimation in this model?

1.       Predict the probabilities of categories Cat and Car of Test2 on the toy corpus example in Sect. 5.3.5.2. You can use the multinomial na¨ıve Bayes model with the same….

### What are the possible advantages and disadvantages of using such an approach?

1.       Na¨ıve Bayes is a generative model in which each class corresponds to one mixture component. Design a semi-supervised generalization of the nai¨ıve Bayes model in which each of the….