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MLF End Term: 30 April 2023, Set QPD1-S2 (January 2023 term)

Question 1

+2 marksOne or more correct options

Which of the following statements regarding classification and regression models are correct ?

Select all that apply.

  1. A

    The task of the regression algorithm is to map the input value (x) with the continuous output variable (y) but the task of the classification algorithm is to map the input value(x) with the discrete output variable(y).

  2. B

    In Classification, we try to find the decision boundary, which can divide the dataset into different classes but In Regression, we try to find the best fit line, which can predict the output more accurately.

  3. C

    The Classification Algorithm can be further divided into Linear and Non-linear classifier and the regression algorithms can be divided into Binary regression and Multi-class regression.

  4. D

    The task of the regression algorithm is to map the input value (x) with the discrete output variable (y) but the task of the classification algorithm is to map the input value(x) with the continuous output variable(y).

Question 2

+3 marksOne correct option
  1. A

    3e

  2. B

    2e

  3. C

    4e

  4. D

    5e

Question 3

+3 marksOne correct option

If A is a 10 × 8 real matrix with rank 8, then which among the following is true?

  1. A
  2. B
  3. C
  4. D

13 more questions in this paper

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More on the MLF End Term 30 Apr 2023 Set QPD1-S2 paper

The IIT Madras BS Machine Learning Foundations (MLF) End Term paper sat on 30 Apr 2023, in the January 2023 term, set QPD1-S2: 16 questions for 50 marks in 180 minutes. The first 3 questions are below. Sign in with Google — it is free — to see the whole paper with its answers and explanations, in learning mode or as a timed mock test.

FeatureMLF End Term 30 Apr 2023 Set QPD1-S2 at a glance
TermJanuary 2023 term
SubjectMachine Learning Foundations
Course codeBSCS2004
Questions16
Marks50
Duration180 min
MSQ5
MCQ7
Numerical4
Official paperIIT M DIPLOMA ET1 EXAM QPD1 S2 30 Apr 2023
Negative markingNo negative marking.
Updated

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