Introduction to Machine Learning Assignment 3 Answers July 2022
Introduction to Machine Learning Assignment 3 Answers 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:-
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
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3. The output of binary class logistic regression lies in this range.
a. [−∞,∞]
b. [−1,1]
c. [0,1]
d. [−∞,0]
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4. If log(1−p(x)1+p(x))=β0+βxlog What is p(x)p(x)?
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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.
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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:-
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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.
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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.
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9. For the two classes ’+’ and ’-’ 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
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10. LDA assumes that the class data is distributed as:
a. Poisson
b. Uniform
c. Gaussian
d. LDA makes no such assumption.
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