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 02
1. Which of the following are the spatial clustering algorithms?
a) Partitioning
based clustering
b) K-means
clustering
c) Grid based
clustering
d) All of the above
Answer: (d) All of the above
Partitioning
based method
The basic idea of
partitioning methods is to construct a partition of a database D into a set
of k clusters where k is a user input parameter. Each of the n objects in D is
assigned to one of the k clusters according to the dissimilarity function. Partitioning
methods are good a t recognizing clusters that are of convex shape, of
similar size, and when k can be reasonably estimated. If k cannot be known ahead of time,
various values of k can be evaluated until the most suitable one is found.
K-means
clustering
It is a type of
partitioning based clustering method.
Grid based
clustering
The grid-based
clustering algorithms quantize the value space into measurable-increments called
a grid structure. The grid structure partitions the value space and
administrates the points through a set of surrounding rectangular-shaped
blocks.
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2. Which of the following tasks can be best solved using Clustering.
a) Predicting the
amount of rainfall based on various cues
b) Detecting fraudulent credit card transactions
c) Training a robot
to solve a maze
d) All of the above
Answer: (b) Detecting fraudulent credit card transactions
Credit card
transactions can be clustered into fraud transactions using unsupervised learning.
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3. Choose the
correct option(s) from the following.
a) When working
with a small dataset, one should prefer low bias/high variance classifiers over
high bias/low variance classifiers.
b) When working with a small dataset, one should prefer high
bias/low variance classifiers over low bias/high variance classifiers.
c) When working
with a large dataset, one should prefer high bias/low variance classifiers over
low bias/high variance classifiers.
d) When working with a large dataset, one should prefer low
bias/high variance classifiers over high bias/low variance classifiers.
Answer: (b) and (d)
On smaller
datasets, variance is a concern since even small changes in the training set
may change the optimal parameters significantly. Hence, a high bias/ low
variance classifier would be preferred. On the other hand, with a large
dataset, since we have sufficient points to represent the data distribution
accurately, variance is not of much concern. Hence, one would go for the
classifier with low bias even though it has higher variance.
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4. Which of the
following best describes the joint probability distribution P(X, Y, Z) for the
given Bayes net.
a) P(X, Y, Z) = P(Y) * P(X|Y) * P(Z|Y)
b) P(X, Y, Z) = P(X)
* P(Y|X) * P(Z|Y)
c) P(X, Y, Z) = P(Z)
* P(X|Z) * P(Y|Z)
d) P(X, Y, Z) = P(X)
* P(Y) * P(Z)
Answer: (a) P(X, Y, Z) = P(Y) * P(X|Y) * P(Z|Y)
In the given network, Y is the start node. X and Z
are nodes where transitions are given from node Y.
Start node Y –
P(Y)
There is a
transition from Y to X. Y is the prior node and X is the current node. So the
probability P(X|Y)
Same for Z. So,
P(Z|Y)
Joint probability
distribution is the products of each probability value.
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5. Compared to the
variance of the Maximum Likelihood Estimate (MLE), the variance of the Maximum
A Posteriori (MAP) estimate is ________
a) higher
b) same
c) lower
d) it could be any
of the above
Answer: (c) lower
The variance of
MAP is lower than that of MLE.
In MLE, the
likelihood is used to estimate whereas in MAP, the likelihood is multiplied
with prior probability. That is, in MAP, the likelihood is weighted by the
prior.
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