We Discuss About That NPTEL Introduction to Machine Learning Assignment 3 Answer
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NPTEL Introduction to Machine Learning Assignment
With the increased availability of data from varied sources there has been increasing attention paid to the various data driven disciplines such as analytics and machine learning. In this course we intend to introduce some of the basic concepts of machine learning from a mathematically well motivated perspective. We will cover the different learning paradigms and some of the more popular algorithms and architectures used in each of these paradigms.
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This course can have Associate in Nursing unproctored programming communication conjointly excluding the Proctored communication, please check announcement section for date and time. The programming communication can have a weightage of twenty fifth towards the ultimate score.
- Assignment score = 25% of average of best 8 assignments out of the total 12 assignments given in the course.
- ( All assignments in a particular week will be counted towards final scoring â quizzes and programming assignments).Â
- Unproctored programming exam score = 25% of the average scores obtained as part of Unproctored programming exam â out of 100
- Proctored Exam score =50% of the proctored certification exam score out of 100
UNPROCTORED PROGRAMMING EXAM SCORE >=10/25 AND PROCTORED EXAM SCORE >= 20/50.Â
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BELOW YOU CAN GET YOUR NPTEL Introduction to Machine Learning Assignment 3 Answer 2022? :
1. For linear classification we use:
a. A linear function to separate the classes.
b. A linear function to model the data.
c. A linear loss.
d. Non-linear function to fit the data.
Answer:- b
2. Logit transformation for Pr(X=1) for given data is S=[0,1,1,0,1,0,1]
a. 3/4
b. 4/3
c. 4/7
d. 3/7
Answer:- c
3. The output of binary class logistic regression lies in this range.
a. [ââ,â]
b. [â1,1]
c. [0,1]
d. [ââ,0]
Answer:- d
4. If log(1âp(x)1+p(x))=ÎČ0+ÎČxlog What is p(x)p(x)?
Answer:- Join Telegram
5. Logistic regression is robust to outliers. Why?
a. The squashing of output values between [0, 1] dampens the affect of outliers.
b. Linear models are robust to outliers.
c. The parameters in logistic regression tend to take small values due to the nature of the problem setting and hence outliers get translated to the same range as other samples.
d. The given statement is false.
Answer:- d
6. Aim of LDA is (multiple options may apply)
a. Minimize intra-class variability.
b. Maximize intra-class variability.
c. Minimize the distance between the mean of classes
d. Maximize the distance between the mean of classes
Answer:-B
7. We have two classes in our dataset with mean 0 and 1, and variance 2 and 3.
a. LDA may be able to classify them perfectly.
b. LDA will definitely be able to classify them perfectly.
c. LDA will definitely NOT be able to classify them perfectly.
d. None of the above.
Answer:- a
8. We have two classes in our dataset with mean 0 and 5, and variance 1 and 2.
a. LDA may be able to classify them perfectly.
b. LDA will definitely be able to classify them perfectly.
c. LDA will definitely NOT be able to classify them perfectly.
d. None of the above.
Answer:- b
9. For the two classes â+â and â-â shown below.
While performing LDA on it, which line is the most appropriate for projecting data points?
a. Red
b. Orange
c. Blue
d. Green
Answer:- b
10. LDA assumes that the class data is distributed as:
a. Poisson
b. Uniform
c. Gaussian
d. LDA makes no such assumption.
Answer:- D
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