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1. You are creating a regression model with the input income, education and current debt of a customer, what could be the possible output from this model.

2. What type of output generated in case of linear regression?

3. If E1 and E2 are two events, how do you represent the conditional probability given that E2 occurs given that E1 has occurred?

4. In which of the scenario you can use the regression to predict the values

5. You are working as a data science consultant for a gaming company. You have three member team and all other stake holders are from the company itself like project managers and project sponsored, data team etc.

During the discussion project managed asked you that when can you tell me that the model you are using is robust enough, after which step you can consider answer for this question?

6. Logistic regression is a model used for prediction of the probability of occurrence of an event. It makes use of several variables that may be......

7. Select the statement which applies correctly to the Naive Bayes

8. The figure below shows a plot of the data of a data matrix M that is 1000 x 2.





Which line represents the first principal component?

9. Refer to Exhibit





In the exhibit, the x-axis represents the derived probability of a borrower defaulting on a loan. Also in the exhibit, the pink represents borrowers that are known to have not defaulted on their loan, and the blue represents borrowers that are known to have defaulted on their loan.

Which analytical method could produce the probabilities needed to build this exhibit?

10. You are working on a email spam filtering assignment, while working on this you find there is new word e.g. HadoopExam comes in email, and in your solutions you never come across this word before, hence probability of this words is coming in either email could be zero.

So which of the following algorithm can help you to avoid zero probability?


 

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