Friday, May 8, 2020

Machine Learning Multiple Choice Questions and Answers 03

Top 5 Machine Learning Quiz Questions with Answers explanation, Interview questions on machine learning, quiz questions for data scientist answers explained, machine learning exam questions

Machine learning MCQ - Set 03

1. Predicting the amount of rainfall in a region based on various cues is a ______ problem.

a) Supervised learning
b) Unsupervised learning
c) Clustering
d) None of the above
Answer: (a) Supervised learning
Predicting the amount of rainfall in a region based on various cues is a supervised learning problem. To develop a model to predict the rainfall, we need historical data as training data to train the model.

2. A and B are two events. If P(A, B) decreases while P(A) increases, which of the following is true?
a) P(A|B) decreases
b) P(B|A) decreases
c) P(B) decreases
d) All of above
Answer: (b) P(B|A) decreases
The conditional probability equation for joint probability distribution;
P(A, B) = P(A|B)P(B) = P(B|A)P(A).
Let us take the second one;
P(A, B) = P(B|A)P(A).
In this equation, if P(A) increases then, only the decrease in P(B|A) will result in decrease of P(A, B).

3. In building a linear regression model for a particular data set, you observe the coefficient of one of the features having a relatively high negative value. This suggests that

a) This feature has a strong effect on the model (should be retained)
b) This feature does not have a strong effect on the model (should be ignored)
c) It is not possible to comment on the importance of this feature without additional information
d) Nothing can be determined.
Answer: (c) It is not possible to comment on the importance of this feature without additional information
A high magnitude suggests that the feature is important. However, it may be the case that another feature is highly correlated with this feature and it's coefficient also has a high magnitude with the opposite sign, in effect cancelling out the effect of the former. Thus, we cannot really remark on the importance of a feature just because it's coefficient has a relatively large magnitude.
[source: Introduction to machine learning, IITM]

4. After applying a regularization penalty in linear regression, you find that some of the coefficients of w are zeroed out. Which of the following penalties might have been used?

a) L0 norm
b) L1 norm
c) L2 norm
d) either (a) or (b)
Answer: (d) either (A) or (B)
Both the norms L0 and L2 are used to reduce some parameters to zero.

L0 norm:

It is a very simple measure of sparsity of a vetor x, counting the number of nonzero entries in x.
Penalizes theℓ0norm (number of non-zeros)

L1 norm (Lasso regularization)

It shrinks the less important feature’s coefficient to zero. Favors sparse solutions by setting certain coefficients to zero and shrinking the rest
Penalizes the ℓ1-norm of the weight vector (sum of the absolute values)

5. MLE estimates are often undesirable because

a) they are biased
b) they have high variance
c) they are not consistent estimators
d) None of the above
Answer: (b) they have high variance
Variance in Maximum Likelihood Estimate (MLE) is high. High variance indicated measurement uncertainty.


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