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Course NameData Science Foundation Program
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GreyCampus – Data Science Foundation Program Answers

1. Fill in the blanks with the correct option(s): Logistic regression is a ____________ regression technique that is used to model data having a ________ outcome

  • linear, numeric
  • linear, binary
  • nonlinear, numeric
  • nonlinear, binary

2. Which of the following is NOT a supervised learning?

  • PCA
  • Decision Tree
  • Linear Regression
  • Naive Bayesian

3. Which of the following is the method to find the best fit line for data in Linear Regression?

  • Least Square Error
  • Maximum Likelihood
  • Logarithmic Loss
  • Both A and B

4. Which of the following assumption in regression modelling impacts the trade-off between under-fitting and over-fitting the most?

  • The polynomial degree
  • Whether we learn the weights by matrix inversion or gradient descent
  • The use of a constant-term
  • None of the above

5. Which one of the following statements is true regarding residuals in regression analysis?

  • Mean of residuals is always zero
  • Mean of residuals is always less than zero
  • Mean of residuals is always greater than zero
  • There is no such rule for residuals.

6. Which of the one is true about Heteroskedasticity?

  • Linear Regression with varying error terms
  • Linear Regression with constant error terms
  • Linear Regression with zero error terms
  • None of these

7. To test linear relationship of y(dependent) and x(independent) continuous variables, which of the following plot best suited?

  • Scatter plot
  • Bar chart
  • Histograms
  • None of these

8. Which of the following is true about “Ridge” or “Lasso” regression methods in case of feature selection?

  • Ridge regression uses subset selection of features
  • Lasso regression uses subset selection of features
  • Both use subset selection of features
  • None of above

9. Which of the following options is true regarding “Regression” and “Correlation”? Note: y is the dependent variable and x is an independent variable.

  • The relationship is symmetric between x and y in both.
  • The relationship is not symmetric between x and y in both.
  • The relationship is not symmetric between x and y in case of correlation but in case of regression it is symmetric.
  • The relationship is symmetric between x and y in case of correlation but in case of regression it is not symmetric.

10. Which of the following methods does not have a closed form solution for its coefficients?

  • Ridge regression
  • Lasso
  • Both Ridge and Lasso
  • None of both

11. Which of the following step/assumption in regression modeling impacts the trade-off between under-fitting and over-fitting the most?

  • The polynomial degree
  • Whether we learn the weights by matrix inversion or gradient descent
  • The use of a constant-term
  • None of the above

12. Let’s say a “Linear regression” model perfectly fits the training data (train error is zero). Now, Which of the following statement is true?

  • You will always have test error zero
  • You can not have test error zero
  • None of the above
  • Both A and B

13. Which of the following indicates a fairly strong relationship between X and Y?

  • Correlation coefficient = 0.9
  • The p-value for the null hypothesis Beta coefficient =0 is 0.0001
  • The t-statistic for the null hypothesis Beta coefficient=0 is 30
  • None of these

14. Which of the following algorithm are not an example of an ensemble learning algorithm?

  • Random Forest
  • Extra Trees
  • Gradient Boosting
  • Decision Trees

15. Which of the following is/are true while applying bagging to regression trees? 1.We build the N regression with N bootstrap sample. 2.We take the average the of N regression tree. 3. Each tree has a high variance with low bias.

  • 1 and 2
  • 2 and 3
  • 1 and 3
  • 1,2 and 3

16. How to select best hyperparameters in tree based models?

  • Measure performance over training data
  • Measure performance over validation data
  • Both of these
  • None of these

17. What are tree based classifiers?

  • Classifiers which form a tree with each attribute at one level.
  • Classifiers which perform series of condition checking with one attribute at a time.
  • Both the options given above.
  • None of the above

18. How will you counter over-fitting in decision tree?

  • By pruning the longer rules
  • By creating new rules
  • Both By pruning the longer rules’ and ‘ By creating new rules’
  • None of the option

19. Which of the following sentence(s) is/are correct?

  • In pre-pruning a tree is ‘pruned’ by halting its construction early.
  • A pruning set of class labeled tuples is used to estimate cost complexity.
  • The best pruned tree is the one that minimizes the number of encoding bits.
  • All of the above

20. Which one of these is not a tree based learner?

  • CART
  • ID3
  • Bayesian Classifier
  • Random Forest


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