Machine Learning Set 3

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Free Online Best Machine Learning MCQ Questions for improve your basic knowledge of Machine Learning. This Machine Learning Set 3 test that contains 25 Multiple Choice Questions with 4 options. You have to select the right answer to a question.

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Question 1

Machine learning applications________

A	Self-driving Cars
B	Speech Recognition
C	Mobile Apps
D	All of the Above

Question 2

Which of the following techniques can not be used for normalization in text mining?

A	Stemming
B	Lemmatization
C	Stop Word Removal
D	None of the above

Question 3

Which of the following is a widely used and effective machine learning algorithm based on the idea of bagging?

A	Decision Tree
B	Regression
C	Classification
D	Random Forest

Question 4

To find the minimum or the maximum of a function, we set the gradient to zero because________

A	The value of the gradient at extrema of a function is always zero
B	Depends on the type of problem
C	Both A and B
D	None of the above

Question 5

Which of the following is a disadvantage of decision trees?

A	Factor analysis
B	Decision trees are robust to outliers
C	Decision trees are prone to be overfit
D	None of the above

Question 6

In which of the following cases will K-means clustering fail to give good results? 1) Data points with outliers 2) Data points with different densities 3) Data points with nonconvex shapes

A	1 and 2
B	2 and 3
C	1 and 3
D	All of the above

Question 7

What is a sentence parser typically used for?

A	It is used to parse sentences to check if they are utf-8 compliant.
B	It is used to parse sentences to derive their most likely syntax tree structures.
C	It is used to parse sentences to assign POS tags to all tokens.
D	It is used to check if sentences can be parsed into meaningful tokens.

Question 8

Suppose we would like to perform clustering on spatial data such as the geometrical locations of houses. We wish to produce clusters of many different sizes and shapes. Which of the following methods is the most appropriate?

A	Decision Trees
B	Density-based clustering
C	Model-based clustering
D	K-means clustering

Question 9

What is K-Nearest Neighbours ?

A	kNN is a statistical technique that can be used for solving for classification and regression problems
B	KNN is Inductive learning
C	KNN is Regular Hypothesis
D	KNN is is used for creating classifiers

Question 10

The skills that you need to acquire for becoming an expert in Machine Learning are____

A	Statistics,Probability Theories
B	Calculus,Optimization
C	techniques,Visualization
D	All of the Above

Question 11

Disadvantages of Deep Learning_____

A	Black Box approach
B	Duration of Development
C	Amount of Data,Computationally Expensive
D	All of the Above

Question 12

Algorithms for Supervised Learning_______

A	K-Nearest Neighbours,Decision Trees
B	Naive Bayes,Logistic Regression
C	Support Vector Machines
D	All of the Above

Question 13

If N is the number of instances in the training dataset, nearest neighbors has a classification run time of

A	O(1)
B	O( N )
C	O(log N )
D	O( N 2 )

Question 14

In Model based learning methods, an iterative process takes place on the ML models that are built based on various model parameters, called ?

A	mini-batches
B	optimizedparameters
C	hyperparameters
D	superparameters

Question 15

Which of the following is a reasonable way to select the number of principal components "k"?

A	Choose k to be the smallest value so that at least 99% of the varinace is retained.
B	Choose k to be 99% of m (k = 0.99*m, rounded to the nearest integer).
C	Choose k to be the largest value so that 99% of the variance is retained.
D	Use the elbow method.

Question 16

Which among the following is/are some of the assumptions made by the k-means algorithm (assuming Euclidean distance measure)?

A	Clusters are spherical in shape
B	Clusters are of similar sizes
C	Data points in one cluster are well separated from data points of other clusters
D	There is no wide variation in density among the data points

Question 17

What is Naive Bayes?

A	Naive Bayes is used for creating classifiers
B	Naive Bayes is a statistical technique that can be used for solving for classification and regression problems
C	Naive Bayes is Inductive learning
D	Naive Bayes is Regular Hypothesis

Question 18

A model of language consists of the categories which does not include______

A	System Unit
B	structural units.
C	data units
D	empirical units

Question 19

Define Data?

A	DATA can be any unprocessed fact, value, text, sound or picture that is not being interpreted and analyzed
B	Data that has been interpreted and manipulated and has now some meaningful inference for the users
C	Combination of inferred information, experiences, learning and insights. Results in awareness or concept building for an individual or organization
D	All of the Above

Question 20

Which of the following is more appropriate to do feature selection?

A	Ridge
B	Lasso
C	both (a) and (b)
D	neither (a) nor (b)

Question 21

The action _______ of a robot arm specify to Place block A on block B.

A	STACK(A,B)
B	LIST(A,B)
C	QUEUE(A,B)
D	ARRAY(A,B)

Question 22

p ? 0q is not a?

A	hack clause
B	horn clause
C	structural clause
D	system clause

Question 23

How do you handle missing or corrupted data in a dataset?

A	Drop missing rows or columns
B	Replace missing values with mean/median/mode
C	Assign a unique category to missing values
D	All of the above

Question 24

When performing regression or classification, which of the following is the correct way to preprocess the data?

A	Normalize the data -> PCA -> training
B	PCA -> normalize PCA output -> training
C	Normalize the data -> PCA -> normalize PCA output -> training
D	None of the above

Question 25

Which of the following statements about regularization is not correct?

A	Using too large a value of lambda can cause your hypothesis to underfit the data.
B	Using too large a value of lambda can cause your hypothesis to overfit the data
C	Using a very large value of lambda cannot hurt the performance of your hypothesis.
D	None of the above

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