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# 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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