940 + Mcqs in Machine Learning McqOptions

1.	scikit-learn also provides a class for per-sample normalization, Normalizer. It can apply________to each element of a dataset
A.	max, l0 and l1 norms
B.	max, l1 and l2 norms
C.	max, l2 and l3 norms
D.	max, l3 and l4 norms
Answer» C. max, l2 and l3 norms

Discussion

2.	How it's possible to use a different placeholder through the parameter_______.
A.	regression
B.	classification
C.	random_state
D.	missing_values
Answer» E.

Discussion

3.	________is much more difficult because it's necessary to determine a supervised strategy to train a model for each feature and, finally, to predict their value
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» C. Using an automatic strategy to input them according to the other known values

Discussion

4.	What is ‘Test set’?
A.	Test set is used to test the accuracy of the hypotheses generated by the learner.
B.	It is a set of data is used to discover the potentially predictive relationship.
C.	Both A & B
D.	None of above
Answer» B. It is a set of data is used to discover the potentially predictive relationship.

Discussion

5.	What is ‘Overfitting’ in Machine learning?
A.	when a statistical model describes random error or noise instead of underlying relationship ‘overfitting’ occurs.
B.	Robots are programed so that they can perform the task based on data they gather from sensors.
C.	While involving the process of learning ‘overfitting’ occurs.
D.	a set of data is used to discover the potentially predictive relationship
Answer» B. Robots are programed so that they can perform the task based on data they gather from sensors.

Discussion

6.	During the last few years, many ______ algorithms have been applied to deepneural networks to learn the best policy for playing Atari video games and to teach an agent how to associate the right action with an input representing the state.
A.	Logical
B.	Classical
C.	Classification
D.	None of above
Answer» E.

Discussion

7.	if there is only a discrete number of possible outcomes (called categories),the process becomes a______.
A.	Regression
B.	Classification.
C.	Modelfree
D.	Categories
Answer» C. Modelfree

Discussion

8.	Reinforcement learning is particularly efficient when______________.
A.	the environment is not completely deterministic
B.	it\s often very dynamic
C.	it\s impossible to have a precise error measure
D.	All above
Answer» E.

Discussion

9.	Common deep learning applications include____
A.	Image classification,Real-time visual tracking
B.	Autonomous car driving,Logistic optimization
C.	Bioinformatics,Speech recognition
D.	All above
Answer» E.

Discussion

10.	What is ‘Training set’?
A.	Training set is used to test the accuracy of the hypotheses generated by the learner.
B.	A set of data is used to discover the potentially predictive relationship.
C.	Both A & B
D.	None of above
Answer» C. Both A & B

Discussion

11.	According to____ , it’s a key success factor for the survival and evolution of all species.
A.	Claude Shannon\s theory
B.	Gini Index
C.	Darwin’s theory
D.	None of above
Answer» D. None of above

Discussion

12.	In reinforcement learning if feedback is negative one it is defined as____.
A.	Penalty
B.	Overlearning
C.	Reward
D.	None of above
Answer» B. Overlearning

Discussion

13.	In order to assess how much information is brought by each component, and the correlation among them, a useful tool is the .
A.	concuttent matrix
B.	convergance matrix
C.	supportive matrix
D.	covariance matrix
Answer» E.

Discussion

14.	dataset with many features contains information proportional to the independence of all features and their variance.
A.	normalized
B.	unnormalized
C.	both a & b
D.	none of the mentioned
Answer» C. both a & b

Discussion

15.	scikit-learn also provides a class for per- sample normalization,
A.	normalizer
B.	imputer
C.	classifier
D.	all above
Answer» B. imputer

Discussion

16.	Which of the following scale data by removing elements that don't belong to a given range or by considering a maximum absolute value.
A.	minmaxscaler
B.	maxabsscaler
C.	both a & b
D.	none of the mentioned
Answer» D. none of the mentioned

Discussion

17.	scikit-learn offers the class , which is responsible for filling the holes using a strategy based on the mean, median, or frequency
A.	labelencoder
B.	labelbinarizer
C.	dictvectorizer
D.	imputer
Answer» E.

Discussion

18.	produce sparse matrices of real numbers that can be fed into any machine learning model.
A.	dictvectorizer
B.	featurehasher
C.	both a & b
D.	none of the mentioned
Answer» D. none of the mentioned

Discussion

19.	While using all labels are turned into sequential numbers.
A.	labelencoder class
B.	labelbinarizer class
C.	dictvectorizer
D.	featurehasher
Answer» B. labelbinarizer class

Discussion

20.	provides some built-in datasets that can be used for testing purposes.
A.	scikit-learn
B.	classification
C.	regression
D.	none of the above
Answer» B. classification

Discussion

21.	Which of the following are several models
A.	regression
B.	classification
C.	none of the above
Answer» D.

Discussion

22.	While using feature selection on the data, is the number of features decreases.
A.	no
B.	yes
Answer» C.

Discussion

23.	Can we extract knowledge without apply feature selection
A.	yes
B.	no
Answer» B. no

Discussion

24.	Which of the following model model include a backwards elimination feature selection routine?
A.	mcv
B.	mars
C.	mcrs
D.	all above
Answer» C. mcrs

Discussion

25.	Which of the following is an example of a deterministic algorithm?
A.	pca
B.	k-means
C.	none of the above
Answer» B. k-means

Discussion

26.	overlearning causes due to an excessive .
A.	capacity
B.	regression
C.	reinforcement
D.	accuracy
Answer» B. regression

Discussion

27.	A supervised scenario is characterized by the concept of a .
A.	programmer
B.	teacher
C.	author
D.	farmer
Answer» C. author

Discussion

28.	According to , its a key success factor for the survival and evolution of all species.
A.	claude shannon\s theory
B.	gini index
C.	darwins theory
D.	none of above
Answer» D. none of above

Discussion

29.	In reinforcement learning if feedback is negative one it is defined as .
A.	penalty
B.	overlearning
C.	reward
D.	none of above
Answer» B. overlearning

Discussion

30.	Techniques involve the usage of both labeled and unlabeled data is called .
A.	supervised
B.	semi-supervised
C.	unsupervised
D.	none of the above
Answer» C. unsupervised

Discussion

31.	When it is necessary to allow the model to develop a generalization ability and avoid a common problem called .
A.	overfitting
B.	overlearning
C.	classification
D.	regression
Answer» B. overlearning

Discussion

32.	What does learning exactly mean?
A.	robots are programed so that they can perform the task based on data they gather from sensors.
B.	a set of data is used to discover the potentially predictive relationship.
C.	learning is the ability to change according to external stimuli and remembering most of all previous experiences.
D.	it is a set of data is used to discover the potentially predictive relationship.
Answer» D. it is a set of data is used to discover the potentially predictive relationship.

Discussion

33.	Even if there are no actual supervisors learning is also based on feedback provided by the environment
A.	supervised
B.	reinforcement
C.	unsupervised
D.	none of the above
Answer» C. unsupervised

Discussion

34.	Which are two techniques of Machine Learning ?
A.	genetic programming and inductive learning
B.	speech recognition and regression
C.	both a & b
D.	none of the mentioned
Answer» B. speech recognition and regression

Discussion

35.	What is Model Selection in Machine Learning?
A.	the process of selecting models among different mathematical models, which are used to describe the same data set
B.	when a statistical model describes random error or noise instead of underlying relationship
C.	find interesting directions in data and find novel observations/ database cleaning
D.	all above
Answer» B. when a statistical model describes random error or noise instead of underlying relationship

Discussion

36.	Which of the following is not Machine Learning?
A.	artificial intelligence
B.	rule based inference
C.	both a & b
D.	none of the mentioned
Answer» C. both a & b

Discussion

37.	What is the standard approach to supervised learning?
A.	split the set of example into the training set and the test
B.	group the set of example into the training set and the test
C.	a set of observed instances tries to induce a general rule
D.	learns programs from data
Answer» B. group the set of example into the training set and the test

Discussion

38.	What are the different Algorithm techniques in Machine Learning?
A.	supervised learning and semi-supervised learning
B.	unsupervised learning and transduction
C.	both a & b
D.	none of the mentioned
Answer» D. none of the mentioned

Discussion

39.	The linearSVMclassifier works by drawing a straight line between two classes
A.	true
B.	false
Answer» B. false

Discussion

40.	SVM is a learning
A.	supervised
B.	unsupervised
C.	both
D.	none
Answer» B. unsupervised

Discussion

41.	SVM is a algorithm
A.	classification
B.	clustering
C.	regression
D.	all
Answer» B. clustering

Discussion

42.	Solving a non linear separation problem with a hard margin Kernelized SVM (Gaussian RBF Kernel) might lead to overfitting
A.	true
B.	false
Answer» B. false

Discussion

43.	Any linear combination of the components of a multivariate Gaussian is a univariate Gaussian.
A.	true
B.	false
Answer» B. false

Discussion

44.	SVMs directly give us the posterior probabilities P(y = 1jx) and P(y = ??1jx)
A.	true
B.	false
Answer» C.

Discussion

45.	Gaussian distribution when plotted, gives a bell shaped curve which is symmetric about the of the feature values.
A.	mean
B.	variance
C.	discrete
D.	random
Answer» B. variance

Discussion

46.	Binarize parameter in BernoulliNB scikit sets threshold for binarizing of sample features.
A.	true
B.	false
Answer» B. false

Discussion

47.	Gaussian Nave Bayes Classifier is distribution
A.	continuous
B.	discrete
C.	binary
Answer» B. discrete

Discussion

48.	Multinomial Nave Bayes Classifier is distribution
A.	continuous
B.	discrete
C.	binary
Answer» C. binary

Discussion

49.	Bernoulli Nave Bayes Classifier is distribution
A.	continuous
B.	discrete
C.	binary
Answer» D.

Discussion

50.	Bayes theorem describes the probability of an event, based on prior knowledge of conditions that might be related to the event.
A.	true
B.	false
Answer» B. false

Discussion

Explore topic-wise MCQs in Testing Subject.

scikit-learn also provides a class for per-sample normalization, Normalizer. It can apply________to each element of a dataset

How it's possible to use a different placeholder through the parameter_______.

________is much more difficult because it's necessary to determine a supervised strategy to train a model for each feature and, finally, to predict their value

What is ‘Test set’?

What is ‘Overfitting’ in Machine learning?

During the last few years, many ______ algorithms have been applied to deepneural networks to learn the best policy for playing Atari video games and to teach an agent how to associate the right action with an input representing the state.

if there is only a discrete number of possible outcomes (called categories),the process becomes a______.

Reinforcement learning is particularly efficient when______________.

Common deep learning applications include____

What is ‘Training set’?

According to____ , it’s a key success factor for the survival and evolution of all species.

In reinforcement learning if feedback is negative one it is defined as____.

In order to assess how much information is brought by each component, and the correlation among them, a useful tool is the .

dataset with many features contains information proportional to the independence of all features and their variance.

scikit-learn also provides a class for per- sample normalization,

Which of the following scale data by removing elements that don't belong to a given range or by considering a maximum absolute value.

scikit-learn offers the class , which is responsible for filling the holes using a strategy based on the mean, median, or frequency

produce sparse matrices of real numbers that can be fed into any machine learning model.

While using all labels are turned into sequential numbers.

provides some built-in datasets that can be used for testing purposes.

Which of the following are several models

While using feature selection on the data, is the number of features decreases.

Can we extract knowledge without apply feature selection

Which of the following model model include a backwards elimination feature selection routine?

Which of the following is an example of a deterministic algorithm?

overlearning causes due to an excessive .

A supervised scenario is characterized by the concept of a .

According to , its a key success factor for the survival and evolution of all species.

In reinforcement learning if feedback is negative one it is defined as .

Techniques involve the usage of both labeled and unlabeled data is called .

When it is necessary to allow the model to develop a generalization ability and avoid a common problem called .

What does learning exactly mean?

Even if there are no actual supervisors learning is also based on feedback provided by the environment

Which are two techniques of Machine Learning ?

What is Model Selection in Machine Learning?

Which of the following is not Machine Learning?

What is the standard approach to supervised learning?

What are the different Algorithm techniques in Machine Learning?

The linearSVMclassifier works by drawing a straight line between two classes

SVM is a learning

SVM is a algorithm

Solving a non linear separation problem with a hard margin Kernelized SVM (Gaussian RBF Kernel) might lead to overfitting

Any linear combination of the components of a multivariate Gaussian is a univariate Gaussian.

SVMs directly give us the posterior probabilities P(y = 1jx) and P(y = ??1jx)

Gaussian distribution when plotted, gives a bell shaped curve which is symmetric about the of the feature values.

Binarize parameter in BernoulliNB scikit sets threshold for binarizing of sample features.

Gaussian Nave Bayes Classifier is distribution

Multinomial Nave Bayes Classifier is distribution

Bernoulli Nave Bayes Classifier is distribution

Bayes theorem describes the probability of an event, based on prior knowledge of conditions that might be related to the event.