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.
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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).
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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]
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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)
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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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