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IIT M QUIZ 1 FOUNDATION DAD DIPLOMA QPE1 16 Oct 2022 - 16 Oct 2022

MLP Quiz 121 questions50 marks19 MCQ · 1 MSQ · 1 SAFoundation / Diploma / QPE1
Elapsed 00:00
Q1MCQ0 marks
question figure
  1. YES
  2. NO
Q2MCQ1 mark
Which of the following options represent the possible type of machine learning algorithm does the cluster plot shown in Figure 2 demonstrate? question figure Which of the following options represent the possible type of machine learning algorithm does the cluster plot shown in Figure 2 demonstrate?
  1. K-means clustering
  2. Linear regression
  3. Logistic regression
  4. PCA
Q3MCQ2 marks
What will be the output of the following code? question figure What will be the output of the following code?
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Q4MCQ2 marks
Which of the following code blocks will generate a random n-class classification problem having a total number of features as 5 and 3 unique labels in the target variable?
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Q5MCQ2 marks
Go through the code snippet given below: question figure Go through the code snippet given below: What will be the shape of the feature matrix (X.shape) and the label vector (y.shape)?
  1. (2000, 28) and (2000, 1)
  2. (28, 2000) and (1505,1)
  3. (500, 7) and (500,)
  4. (2000, 7) and (1505,)
Q6MCQ2 marks
Which of the following options is likely to be the correct output of the code snippet given below? question figure Which of the following options is likely to be the correct output of the code snippet given below?
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Q7MCQ2 marks
Consider the following code snippet: question figure Consider the following code snippet: Above code returns (178, 13) as output. Which of the following would be the correct code(s) to split X and y such that you have exactly 36 samples for test data?
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Q8MCQ2 marks
In the figure 1, what are the names of classes arranged in the decreasing order of the median values? question figure In the figure 1, what are the names of classes arranged in the decreasing order of the median values?
  1. First > Second > Third
  2. Third > Second > First
  3. Second < Third < First
  4. Third > First > Second
Q9MCQ2 marks
Which of the following is likely to be the correct output of the code given below? question figure Which of the following is likely to be the correct output of the code given below?
  1. 0.12
  2. 190.31
  3. 0.93
  4. 90.02
  5. No evaluation metrics is mentioned, hence it will produce error
Q10MCQ3 marks
Which of the following is likely to be the correct output of the code given below? question figure Which of the following is likely to be the correct output of the code given below?
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Q11MCQ3 marks
Consider the following code snippet: question figure Consider the following code snippet: Which of the following could be the correct output?
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Q12MCQ3 marks
Which of the following options will be the correct output of print(clf.coef_)? question figure Which of the following options will be the correct output of print(clf.coef_)?
  1. [ 0.35 -0. -0. ]
  2. [-0.85,0]
  3. [3,2,1,2]
  4. Given code block will return an error.
Q13MCQ3 marks
Go through the code snippet given below and mark the correct output. question figure Go through the code snippet given below and mark the correct output.
  1. 1
  2. 3.5
  3. 5
  4. 25
  5. 4
  6. 12.75
Q14MCQ3 marks
Go through the code snippet given below and mark the correct output. question figure Go through the code snippet given below and mark the correct output.
  1. 0.61
  2. 0.83
  3. 0.91
  4. 1
  5. -3
  6. 2
Q15MCQ3 marks
Which of the following is likely to be the correct output of the code given below? question figure Which of the following is likely to be the correct output of the code given below?
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Q16MCQ3 marks
Which of the following code blocks will correctly take the learning rate as ‘invscaling’ ?
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Q17MCQ3 marks
Go through the code snippet given below and answer the following questions. question figure Go through the code snippet given below and answer the following questions. Which of the following could be the possible output of print(results.alpha_)?
  1. -0.528
  2. -5
  3. 0.528
  4. 1.e-03
  5. 0.01
Q18MSQ3 marksselect all that apply
Scikit-Learn’s API is remarkably well designed. The main design principles are:-
  1. Nonproliferation of classes
  2. Best Estimator
  3. Proportion
  4. Consistency
Q19SA2 marks
Enter the correct output of the following code block. question figure Enter the correct output of the following code block.
Comprehension passage - the questions below it refer to this
Go through the code snippet given below and answer the given subquestions. question figure Go through the code snippet given below and answer the given subquestions.
Q20MCQ3 marks
Which of the following options will be the output of the given code?
  1. array([ 0.02645639, -0.09121131, 0.09350173, 0.11821129])
  2. [-0.05102706 0.11483443 -0.01544466 0.04813268 0.05634579]
  3. array([-0.22622766, -0.0, -0.0])
  4. Given code will return an error because the data-set is not given.
Q21MCQ3 marks
Which of the following could be the possible output of print(reg.score())?
  1. -0.528
  2. 1
  3. 0.528
  4. Given code will return an error

Answer key

  1. Q1 (MCQ, 0 marks): A
  2. Q2 (MCQ, 1 marks): A
  3. Q3 (MCQ, 2 marks): B
  4. Q4 (MCQ, 2 marks): C
  5. Q5 (MCQ, 2 marks): C
  6. Q6 (MCQ, 2 marks): D
  7. Q7 (MCQ, 2 marks): C
  8. Q8 (MCQ, 2 marks): A
  9. Q9 (MCQ, 2 marks): D
  10. Q10 (MCQ, 3 marks): C
  11. Q11 (MCQ, 3 marks): A
  12. Q12 (MCQ, 3 marks): D
  13. Q13 (MCQ, 3 marks): F
  14. Q14 (MCQ, 3 marks): E
  15. Q15 (MCQ, 3 marks): A
  16. Q16 (MCQ, 3 marks): C
  17. Q17 (MCQ, 3 marks): E
  18. Q18 (MSQ, 3 marks): A, D
  19. Q19 (SA, 2 marks): 8
  20. Q20 (MCQ, 3 marks): B
  21. Q21 (MCQ, 3 marks): D

Concepts this paper tests

Auto-generated summary from the source site. Handy as a checklist; the lessons above explain each idea from scratch.

Summary of Core Topics, Concepts, Principles, and Equations

1. Machine Learning Algorithms

2. Data Preprocessing

3. Model Evaluation

4. Data Splitting

5. Data Generation

6. Scikit-Learn API Design Principles

7. Equations

MSE=1n∑i=1n(yi−y^i)2\text{MSE} = \frac{1}{n} \sum_{i=1}^{n} (y_i - \hat{y}_i)^2
R2=1−∑i=1n(yi−y^i)2∑i=1n(yi−yˉ)2R^2 = 1 - \frac{\sum_{i=1}^{n} (y_i - \hat{y}_i)^2}{\sum_{i=1}^{n} (y_i - \bar{y})^2}
Cost Function=∑i=1n(yi−y^i)2+α∑j=1mθj2\text{Cost Function} = \sum_{i=1}^{n} (y_i - \hat{y}_i)^2 + \alpha \sum_{j=1}^{m} \theta_j^2
Cost Function=∑i=1n(yi−y^i)2+α∑j=1m∣θj∣\text{Cost Function} = \sum_{i=1}^{n} (y_i - \hat{y}_i)^2 + \alpha \sum_{j=1}^{m} |\theta_j|

Graph 1: Machine Learning Algorithms

Graph 2: Data Preprocessing

Graph 3: Model Evaluation

Graph 4: Data Splitting

Graph 5: Data Generation

Graph 6: Scikit-Learn API Design Principles

These graphs and summaries should help you understand the core topics and their relationships, which are essential for the exam.