Advanced Database Management System - Tutorials and Notes: Machine Learning Multiple Choice Questions and Answers 02

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Friday, 8 May 2020

Machine Learning Multiple Choice Questions and Answers 02

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.

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.

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.

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.

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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