what is the function of Unsupervised Learning?

find clusters of the data and find low-dimensional representations of the data

find interesting directions in data and find novel observations/ database cleaning

interesting coordinates and correlations

Identify the various approaches for machine learning.

concept vs classification learning

symbolic vs statistical learning

inductive vs analytical learning

Common deep learning applications / problems can also be solved using

real-time visual object identification

The SVMs are less effective when:

the data is clean and ready to use

the data is noisy and contains overlapping points

Suppose you are building a SVM model on data X. The data X can be error prone which means that you should not trust any specific data point too much. Now think that you want to build a SVM model which has quadratic kernel function of polynomial degree 2 that uses Slack variable C as one of its hyper parameter.What would happen when you use very small C (C~0)?

data will be correctly classified

Which of the following is true aboutRidge or Lasso regression methods in case of feature selection?

both use subset selection of features

ridge regression uses subset selection of features

lasso regression uses subset selection of features

both use subset selection of features

which of the following step / assumption in regression modeling impacts the trade- off between under-fitting and over-fitting the most.

whether we learn the weights by matrix inversion or gradient descent

Which of the one is true about Heteroskedasticity?

linear regression with constant error terms

linear regression with varying error terms

linear regression with constant error terms

linear regression with zero error terms

In a linear regression problem, we are using R-squared to measure goodness-of-fit. We add a feature in linear regression model and retrain the same model.Which of the following option is true?

if r squared increases, this variable is significant.

if r squared decreases, this variable is not significant.

individually r squared cannot tell about variable importance. we cant say anything about it right now.

Lets say, a Linear regression model perfectly fits the training data (train error is zero). Now, Which of the following statement is true?

you will always have test error zero

you can not have test error zero

Suppose you have fitted a complex regression model on a dataset. Now, you are using Ridge regression with tuning parameter lambda to reduce its complexity. Choose the option(s) below which describes relationship of bias and variance with lambda.

In case of very large lambda; bias is high, variance is high

In case of very large lambda; bias is low, variance is low

In case of very large lambda; bias is low, variance is high

In case of very large lambda; bias is high, variance is low

In case of very large lambda; bias is high, variance is high

______is the most drastic one and should be considered only when the dataset is quite large, the number of missing features is high, and any prediction could be risky.

Creating sub-model to predict those features

Using an automatic strategy to input them according to the other known values

Which of the following is true about Naive Bayes ?

Assumes that all the features in a dataset are equally important

Assumes that all the features in a dataset are independent

What is the purpose of performing cross-validation?

a. To assess the predictive performance of the models

b. To judge how the trained model performs outside the sample on test data

Suppose you are building a SVM model on data X. The data X can be error prone which means that you should not trust any specific data point too much. Now think that you want to build a SVM model which has quadratic kernel function of polynomial degree 2 that uses Slack variable C as one of it’s hyper parameter.What would happen when you use very large value of C(C->infinity)?

We can not classify data correctly for given setting of hyper parameter C

We can still classify data correctly for given setting of hyper parameter C

We can not classify data correctly for given setting of hyper parameter C

Which of the following option is true regarding “Regression” and “Correlation” ?Note: y is dependent variable and x is independent variable.

A. The relationship is symmetric between x and y in both.

B. The relationship is not symmetric between x and y in both.

C. The relationship is not symmetric between x and y in case of correlation but in case of regression it is symmetric.

D. The relationship is symmetric between x and y in case of correlation but in case of regression it is not symmetric.

When the C parameter is set to infinite, which of the following holds true?

The soft-margin classifier will separate the data

The optimal hyperplane if exists, will be the one that completely separates the data

The soft-margin classifier will separate the data

Suppose we fit “Lasso Regression” to a data set, which has 100 features (X1,X2…X100). Now, we rescale one of these feature by multiplying with 10 (say that feature is X1), and then refit Lasso regression with the same regularization parameter.Now, which of the following option will be correct?

A. It is more likely for X1 to be excluded from the model

B. It is more likely for X1 to be included in the model

What are the two methods used for the calibration in Supervised Learning?

Statistics andInformal Retrieval

Platt Calibration and Isotonic Regression

Statistics andInformal Retrieval

what is the function of ‘Unsupervised Learning’?

Find clusters of the data and find low-dimensional representations of the data

Find interesting directions in data and find novel observations/ database cleaning

Interesting coordinates and correlations

Common deep learning applications / problems can also be solved using____

Real-time visual object identification

940 + Mcqs in Machine Learning in Artificial Intelligence Page 3 McqOptions

101.	what is the function of Unsupervised Learning?
A.	find clusters of the data and find low-dimensional representations of the data
B.	find interesting directions in data and find novel observations/ database cleaning
C.	interesting coordinates and correlations
D.	all
Answer» E.

Discussion

102.	Identify the various approaches for machine learning.
A.	concept vs classification learning
B.	symbolic vs statistical learning
C.	inductive vs analytical learning
D.	all above
Answer» E.

Discussion

103.	Common deep learning applications / problems can also be solved using
A.	real-time visual object identification
B.	classic approaches
C.	automatic labeling
D.	bio-inspired adaptive systems
Answer» C. automatic labeling

Discussion

104.	The term can be freely used, but with the same meaning adopted in physics or system theory.
A.	accuracy
B.	cluster
C.	regression
D.	prediction
Answer» E.

Discussion

105.	Some people are using the term instead of prediction only to avoid the weird idea that machine learning is a sort of modern magic.
A.	inference
B.	interference
C.	accuracy
D.	none of above
Answer» B. interference

Discussion

106.	If there is only a discrete number of possible outcomes called .
A.	modelfree
B.	categories
C.	prediction
D.	none of above
Answer» C. prediction

Discussion

107.	The SVMs are less effective when:
A.	the data is linearly separable
B.	the data is clean and ready to use
C.	the data is noisy and contains overlapping points
Answer» D.

Discussion

108.	If you remove the non-red circled points from the data, the decision boundary will
A.	true
B.	false
Answer» C.

Discussion

109.	Suppose you are building a SVM model on data X. The data X can be error prone which means that you should not trust any specific data point too much. Now think that you want to build a SVM model which has quadratic kernel function of polynomial degree 2 that uses Slack variable C as one of its hyper parameter.What would happen when you use very small C (C~0)?
A.	misclassification would happen
B.	data will be correctly classified
C.	cant say
D.	none of these
Answer» B. data will be correctly classified

Discussion

110.	What is/are true about kernel in SVM?1. Kernel function map low dimensional data to high dimensional space2. Its a similarity function
A.	1
B.	2
C.	1 and 2
D.	none of these
Answer» D. none of these

Discussion

111.	How many coefficients do you need to estimate in a simple linear regression model (One independent variable)?
A.	1
B.	2
C.	cant say
Answer» C. cant say

Discussion

112.	Which of the following statement(s) can be true post adding a variable in a linear regression model?1. R-Squared and Adjusted R-squared both increase2. R- Squared increases and Adjusted R-
A.	1 and 2
B.	1 and 3
C.	2 and 4
D.	none of the above
Answer» B. 1 and 3

Discussion

113.	Which of the following is true aboutRidge or Lasso regression methods in case of feature selection?
A.	ridge regression uses subset selection of features
B.	lasso regression uses subset selection of features
C.	both use subset selection of features
D.	none of above
Answer» C. both use subset selection of features

Discussion

114.	Can we calculate the skewness of variables based on mean and median?
A.	true
B.	false
Answer» C.

Discussion

115.	which of the following step / assumption in regression modeling impacts the trade- off between under-fitting and over-fitting the most.
A.	the polynomial degree
B.	whether we learn the weights by matrix inversion or gradient descent
C.	the use of a constant-term
Answer» B. whether we learn the weights by matrix inversion or gradient descent

Discussion

116.	Which of the following assumptions do we make while deriving linear regression parameters?1. The true relationship between dependent y and predictor x is linear2. The model errors are statistically independent3. The errors are normally distributed with a 0 mean and constant standard deviation4. The predictor x is non-stochastic and is measured error-free
A.	1,2 and 3.
B.	1,3 and 4.
C.	1 and 3.
D.	all of above.
Answer» E.

Discussion

117.	Which of the one is true about Heteroskedasticity?
A.	linear regression with varying error terms
B.	linear regression with constant error terms
C.	linear regression with zero error terms
D.	none of these
Answer» B. linear regression with constant error terms

Discussion

118.	In a linear regression problem, we are using R-squared to measure goodness-of-fit. We add a feature in linear regression model and retrain the same model.Which of the following option is true?
A.	if r squared increases, this variable is significant.
B.	if r squared decreases, this variable is not significant.
C.	individually r squared cannot tell about variable importance. we cant say anything about it right now.
D.	none of these.
Answer» D. none of these.

Discussion

119.	Lets say, a Linear regression model perfectly fits the training data (train error is zero). Now, Which of the following statement is true?
A.	you will always have test error zero
B.	you can not have test error zero
C.	none of the above
Answer» D.

Discussion

120.	Suppose, you got a situation where you find that your linear regression model is under fitting the data. In such situation which of the following options would you consider?1. I will add more variables2. I will start introducing polynomial degree variables3. I will remove some variables
A.	1 and 2
B.	2 and 3
C.	1 and 3
D.	1, 2 and 3
Answer» B. 2 and 3

Discussion

121.	Suppose you have fitted a complex regression model on a dataset. Now, you are using Ridge regression with tuning parameter lambda to reduce its complexity. Choose the option(s) below which describes relationship of bias and variance with lambda.
A.	In case of very large lambda; bias is low, variance is low
B.	In case of very large lambda; bias is low, variance is high
C.	In case of very large lambda; bias is high, variance is low
D.	In case of very large lambda; bias is high, variance is high
Answer» D. In case of very large lambda; bias is high, variance is high

Discussion

122.	It's possible to specify if the scaling process must include both mean and standard deviation using the parameters________.
A.	with_mean=True/False
B.	with_std=True/False
C.	Both A & B
D.	None of the Mentioned
Answer» D. None of the Mentioned

Discussion

123.	______is the most drastic one and should be considered only when the dataset is quite large, the number of missing features is high, and any prediction could be risky.
A.	Removing the whole line
B.	Creating sub-model to predict those features
C.	Using an automatic strategy to input them according to the other known values
D.	All above
Answer» B. Creating sub-model to predict those features

Discussion

124.	_____which can accept a NumPy RandomState generator or an integer seed.
A.	make_blobs
B.	random_state
C.	test_size
D.	training_size
Answer» C. test_size

Discussion

125.	In many classification problems, the target dataset is made up of categorical labels which cannot immediately be processed by any algorithm. An encoding is needed and scikit-learn offers at least_____valid options
A.	1
B.	2
C.	3
D.	4
Answer» C. 3

Discussion

126.	allows exploiting the natural sparsity of data while extracting principal components.
A.	sparsepca
B.	kernelpca
C.	svd
D.	init parameter
Answer» B. kernelpca

Discussion

127.	______ showed better performance than other approaches, even without a context-based model
A.	Machine learning
B.	Deep learning
C.	Reinforcement learning
D.	Supervised learning
Answer» C. Reinforcement learning

Discussion

128.	there's a growing interest in pattern recognition and associative memories whose structure and functioning are similar to what happens in the neocortex. Such an approach also allows simpler algorithms called _____
A.	Regression
B.	Accuracy
C.	Modelfree
D.	Scalable
Answer» D. Scalable

Discussion

129.	In the last decade, many researchers started training bigger and bigger models, built with several different layers that's why this approach is called_____.
A.	Deep learning
B.	Machine learning
C.	Reinforcement learning
D.	Unsupervised learning
Answer» B. Machine learning

Discussion

130.	In reinforcement learning, this feedback is usually called as___.
A.	Overfitting
B.	Overlearning
C.	Reward
D.	None of above
Answer» D. None of above

Discussion

131.	______can be adopted when it's necessary to categorize a large amount of data with a few complete examples or when there's the need to impose some constraints to a clustering algorithm.
A.	Supervised
B.	Semi-supervised
C.	Reinforcement
D.	Clusters
Answer» C. Reinforcement

Discussion

132.	Which of the following is true about Naive Bayes ?
A.	Assumes that all the features in a dataset are equally important
B.	Assumes that all the features in a dataset are independent
C.	Both A and B
D.	None of the above option
Answer» D. None of the above option

Discussion

133.	What is the purpose of performing cross-validation?
A.	a. To assess the predictive performance of the models
B.	b. To judge how the trained model performs outside the sample on test data
C.	c. Both A and B
Answer» D.

Discussion

134.	In SVR we try to fit the error within a certain threshold.
A.	true
B.	false
Answer» B. false

Discussion

135.	Hyperplanes are decision boundaries that help classify the data points.
A.	true
B.	false
Answer» B. false

Discussion

136.	The _____of the hyperplane depends upon the number of features.
A.	dimension
B.	classification
C.	reduction
Answer» B. classification

Discussion

137.	SVM algorithms use a set of mathematical functions that are defined as the kernel.
A.	true
B.	false
Answer» B. false

Discussion

138.	Hyperplanes are _____________boundaries that help classify the data points.
A.	usual
B.	decision
C.	parallel
Answer» C. parallel

Discussion

139.	The objective of the support vector machine algorithm is to find a hyperplane in an N-dimensional space(N — the number of features) that distinctly classifies the data points.
A.	true
B.	false
Answer» B. false

Discussion

140.	SVM can solve linear and non-linear problems
A.	true
B.	false
Answer» B. false

Discussion

141.	Suppose you are building a SVM model on data X. The data X can be error prone which means that you should not trust any specific data point too much. Now think that you want to build a SVM model which has quadratic kernel function of polynomial degree 2 that uses Slack variable C as one of it’s hyper parameter.What would happen when you use very large value of C(C->infinity)?
A.	We can still classify data correctly for given setting of hyper parameter C
B.	We can not classify data correctly for given setting of hyper parameter C
C.	Can’t Say
D.	None of these
Answer» B. We can not classify data correctly for given setting of hyper parameter C

Discussion

142.	Which of the following option is true regarding “Regression” and “Correlation” ?Note: y is dependent variable and x is independent variable.
A.	A. The relationship is symmetric between x and y in both.
B.	B. The relationship is not symmetric between x and y in both.
C.	C. The relationship is not symmetric between x and y in case of correlation but in case of regression it is symmetric.
D.	D. The relationship is symmetric between x and y in case of correlation but in case of regression it is not symmetric.
Answer» E.

Discussion

143.	When the C parameter is set to infinite, which of the following holds true?
A.	The optimal hyperplane if exists, will be the one that completely separates the data
B.	The soft-margin classifier will separate the data
C.	None of the above
Answer» B. The soft-margin classifier will separate the data

Discussion

144.	If two variables are correlated, is it necessary that they have a linear relationship?
A.	A. Yes
B.	B. No
Answer» C.

Discussion

145.	We can also compute the coefficient of linear regression with the help of an analytical method called “Normal Equation”. Which of the following is/are true about “Normal Equation”?1. We don’t have to choose the learning rate2. It becomes slow when number of features is very large3. No need to iterate
A.	A. 1 and 2
B.	B. 1 and 3.
C.	C. 2 and 3.
D.	D. 1,2 and 3.
Answer» E.

Discussion

146.	Suppose we fit “Lasso Regression” to a data set, which has 100 features (X1,X2…X100). Now, we rescale one of these feature by multiplying with 10 (say that feature is X1), and then refit Lasso regression with the same regularization parameter.Now, which of the following option will be correct?
A.	A. It is more likely for X1 to be excluded from the model
B.	B. It is more likely for X1 to be included in the model
C.	C. Can’t say
D.	D. None of these
Answer» C. C. Can’t say

Discussion

147.	What are the two methods used for the calibration in Supervised Learning?
A.	Platt Calibration and Isotonic Regression
B.	Statistics andInformal Retrieval
Answer» B. Statistics andInformal Retrieval

Discussion

148.	what is the function of ‘Unsupervised Learning’?
A.	Find clusters of the data and find low-dimensional representations of the data
B.	Find interesting directions in data and find novel observations/ database cleaning
C.	Interesting coordinates and correlations
D.	All
Answer» E.

Discussion

149.	Common deep learning applications / problems can also be solved using____
A.	Real-time visual object identification
B.	Classic approaches
C.	Automatic labeling
D.	Bio-inspired adaptive systems
Answer» C. Automatic labeling

Discussion

150.	The term _____ can be freely used, but with the same meaning adopted in physics or system theory.
A.	Accuracy
B.	Cluster
C.	Regression
D.	Prediction
Answer» E.

Discussion

Explore topic-wise MCQs in Artificial Intelligence.

what is the function of Unsupervised Learning?

Identify the various approaches for machine learning.

Common deep learning applications / problems can also be solved using

The term can be freely used, but with the same meaning adopted in physics or system theory.

Some people are using the term instead of prediction only to avoid the weird idea that machine learning is a sort of modern magic.

If there is only a discrete number of possible outcomes called .

The SVMs are less effective when:

If you remove the non-red circled points from the data, the decision boundary will

What is/are true about kernel in SVM?1. Kernel function map low dimensional data to high dimensional space2. Its a similarity function

How many coefficients do you need to estimate in a simple linear regression model (One independent variable)?

Which of the following statement(s) can be true post adding a variable in a linear regression model?1. R-Squared and Adjusted R-squared both increase2. R- Squared increases and Adjusted R-

Which of the following is true aboutRidge or Lasso regression methods in case of feature selection?

Can we calculate the skewness of variables based on mean and median?

which of the following step / assumption in regression modeling impacts the trade- off between under-fitting and over-fitting the most.

Which of the one is true about Heteroskedasticity?

In a linear regression problem, we are using R-squared to measure goodness-of-fit. We add a feature in linear regression model and retrain the same model.Which of the following option is true?

Lets say, a Linear regression model perfectly fits the training data (train error is zero). Now, Which of the following statement is true?

Suppose, you got a situation where you find that your linear regression model is under fitting the data. In such situation which of the following options would you consider?1. I will add more variables2. I will start introducing polynomial degree variables3. I will remove some variables

Suppose you have fitted a complex regression model on a dataset. Now, you are using Ridge regression with tuning parameter lambda to reduce its complexity. Choose the option(s) below which describes relationship of bias and variance with lambda.

It's possible to specify if the scaling process must include both mean and standard deviation using the parameters________.

______is the most drastic one and should be considered only when the dataset is quite large, the number of missing features is high, and any prediction could be risky.

_____which can accept a NumPy RandomState generator or an integer seed.

In many classification problems, the target dataset is made up of categorical labels which cannot immediately be processed by any algorithm. An encoding is needed and scikit-learn offers at least_____valid options

allows exploiting the natural sparsity of data while extracting principal components.

______ showed better performance than other approaches, even without a context-based model

there's a growing interest in pattern recognition and associative memories whose structure and functioning are similar to what happens in the neocortex. Such an approach also allows simpler algorithms called _____

In the last decade, many researchers started training bigger and bigger models, built with several different layers that's why this approach is called_____.

In reinforcement learning, this feedback is usually called as___.

______can be adopted when it's necessary to categorize a large amount of data with a few complete examples or when there's the need to impose some constraints to a clustering algorithm.

Which of the following is true about Naive Bayes ?

What is the purpose of performing cross-validation?

In SVR we try to fit the error within a certain threshold.

Hyperplanes are decision boundaries that help classify the data points.

The _____of the hyperplane depends upon the number of features.

SVM algorithms use a set of mathematical functions that are defined as the kernel.

Hyperplanes are _____________boundaries that help classify the data points.

The objective of the support vector machine algorithm is to find a hyperplane in an N-dimensional space(N — the number of features) that distinctly classifies the data points.

SVM can solve linear and non-linear problems

Which of the following option is true regarding “Regression” and “Correlation” ?Note: y is dependent variable and x is independent variable.

When the C parameter is set to infinite, which of the following holds true?

If two variables are correlated, is it necessary that they have a linear relationship?

What are the two methods used for the calibration in Supervised Learning?

what is the function of ‘Unsupervised Learning’?

Common deep learning applications / problems can also be solved using____

The term _____ can be freely used, but with the same meaning adopted in physics or system theory.