MLT End Term18 questions100 marks8 MSQ · 6 SA · 4 MCQforenoon
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Q1MSQ5 marksselect all that apply
Comprehension passage - the questions below it refer to this
Based on the above data, answer the given subquestions.
Q2MCQ5 marks
Suppose the team applies linear PCA on the dataset and reconstructs the data points with zero error using k principal components (directions). For which value of k the reconstruction error would become zero?
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5
10
100
Q3MSQ6 marksselect all that apply
Q4SA5 marks
(Enter your answer correctly to two decimal places)
Q5SA6 marks
Q6MSQ6 marksselect all that apply
Q7MCQ6 marks
[2, 6, 9, 12]
[2, 4, 7, 10]
[3, 5, 8, 11]
[1, 3, 6, 8]
Q8SA6 marks
Q9MCQ5 marks
0
1
Insufficient information to make a prediction.
Q10MCQ5 marks
Yes, it will converge.
No, it will never converge.
Insufficient data
Q11SA6 marks
Comprehension passage - the questions below it refer to this
Based on the above data, answer the given subquestions.
Q12MSQ6 marksselect all that apply
What is the equation of the decision boundary? Select all options that are correct.
Q13SA5 marks
Q14MSQ6 marksselect all that apply
Q15MSQ6 marksselect all that apply
Which of the following statements are correct?
Overfitting models have high bias and low variance.
Underfitting models have low bias and high variance.
Generally, weak learners in the random forest tend to overfit.
If the performance of each estimator in the bagging algorithm is almostidentical, the benefit of using bagging to combine them may be minimal or insignificant.
In random forests, multiple decision trees (estimators) are trainedsimultaneously, allowing for parallel processing and faster model training.