Machine Learning MCQs with Answers

1 .

What is Machine learning?

A)

The autonomous acquisition of knowledge through the use of manual programs

B)

The selective acquisition of knowledge through the use of manual programs

C)

The autonomous acquisition of knowledge through the use of computer programs

D)

The selective acquisition of knowledge through the use of computer programs

Correct Answer : The autonomous acquisition of knowledge through the use of computer programs

Explanation : * Machine learning (ML) is the study of computer algorithms that can improve automatically through experience and by the use of data. It is seen as a part of artificial intelligence.

* Machine learning is the autonomous acquisition of knowledge through the use of computer programs.

* Machine learning algorithms build a model based on sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to do so. Machine learning algorithms are used in a wide variety of applications, such as in medicine, email filtering, speech recognition, and computer vision, where it is difficult or unfeasible to develop conventional algorithms to perform the needed tasks.

2 .

How many types of Machine Learning Techniques?

A)

3

B)

5

C)

7

D)

9

Correct Answer : 3

Explanation : There are three(3) types of Machine Learning techniques, which are Supervised Learning, Unsupervised Learning, and Reinforcement Learning.

3 .

Machine learning is a subset of_________

A)

Data Learining

B)

Deep Learning

C)

Artificial Intelligence

D)

None of the above

Correct Answer : Artificial Intelligence

4 .

Machine Learning is a field of AI consisting of learning algorithms that_________

A)

At executing some task

B)

Over time with experience

C)

Improve their performance

D)

All of the above

Correct Answer : All of the above

5 .

What is true about Machine Learning?

A)

Machine Learning (ML) is that field of computer science

B)

ML is a type of artificial intelligence that extract patterns out of raw data by using an algorithm or method.

C)

The main focus of ML is to allow computer systems learn from experience without being explicitly programmed or human intervention.

D)

All of the above

Correct Answer : All of the above

Explanation : All statement are true about Machine Learning.

6 .

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)

hyperparameters

C)

superparameters

D)

optimizedparameters

Correct Answer : hyperparameters

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

7 .

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

A)

Random Forest

B)

Regression

C)

Classification

D)

Decision Tree

Correct Answer : Random Forest

Explanation : The Radom Forest algorithm builds an ensemble of Decision Trees, mostly trained with the bagging method.

8 .

Which of the following statements about regularization is not correct?

A)

Using too large a value of lambda can cause your hypothesis to overfit the data

B)

Using too large a value of lambda can cause your hypothesis to underfit the data.

C)

Using a very large value of lambda cannot hurt the performance of your hypothesis.

D)

None of the above

Correct Answer : None of the above

Explanation : A large value results in a large regularization penalty and therefore, a strong preference for simpler models, which can underfit the data.

9 .

Data points have negetive residual

A)

if they are below the regression line

B)

if they are above the regression line

C)

if the regression line actually passes through the point

D)

None of the above

Correct Answer : if they are below the regression line

10 .

In linear regression, we try to ______ the least square errors of the model to identify the line of best fit.

A)

Change

B)

Maximize

C)

Minimize

D)

None of the above

Correct Answer : Minimize

11 .

Which of the following evaluation metrics can be used to evaluate a model while modeling a continuous output variable?

A)

Logloss

B)

Accuracy

C)

AUC-ROC

D)

Mean-Squared-Error

Correct Answer : Mean-Squared-Error

12 .

Which of the following is true about Residuals?

A)

Higher is better

B)

Lower is better

C)

Both (a) and (b)

D)

None of the above

Correct Answer : Lower is better

13 .

Which of the following is not a supervised learning?

A)

PCA

B)

Naive Bayesian

C)

Linear Regression

D)

Decision Tree

Correct Answer : PCA

14 .

Which of the following are ML methods?

A)

supervised Learning

B)

based on human supervision

C)

semi-reinforcement Learning

D)

All of the above

Correct Answer : based on human supervision

Explanation : The following are various ML methods based on some broad categories : Based on human supervision, Unsupervised Learning, Semi-supervised Learning and Reinforcement Learning

15 .

Different learning methods does not include?

A)

Analogy

B)

Deduction

C)

Introduction

D)

Memorization

Correct Answer : Introduction

16 .

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

Correct Answer : Stop Word Removal

Explanation : Lemmatization and stemming are the techniques of keyword normalization.

17 .

What is a sentence parser typically used for?

A)

It is used to parse sentences to assign POS tags to all tokens.

B)

It is used to parse sentences to check if they are utf-8 compliant.

C)

It is used to check if sentences can be parsed into meaningful tokens.

D)

It is used to parse sentences to derive their most likely syntax tree structures.

Correct Answer : It is used to parse sentences to derive their most likely syntax tree structures.

Explanation : Sentence parsers analyze a sentence and automatically build a syntax tree.

18 .

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

Correct Answer : All of the above

Explaination : K-means clustering algorithm fails to give good results when the data contains outliers, the density spread of data points across the data space is different, and the data points follow nonconvex shapes.

19 .

We can compute the coefficient of linear regression by using

A)

Gradient descent

B)

Normal Equation

C)

Both (a) and (b)

D)

None of the above

Correct Answer : Both (a) and (b)

20 .

Data points have positive residual

A)

if they are above the regression line

B)

if they are below the regression line

C)

if the regression line actually passes through the point

D)

None of the above

Correct Answer : if they are above the regression line

21 .

Data points have zero residual

A)

if they are above the regression line

B)

if they are below the regression line

C)

if the regression line actually passes through the point

D)

None of the above

Correct Answer : if the regression line actually passes through the point

22 .

Which of the following is not numerical functions in the various function representation of Machine Learning?

A)

Neural Network

B)

Case-based

C)

Linear Regression

D)

Support Vector Machines

Correct Answer : Case-based

23 .

Real-Time decisions, Game AI, Learning Tasks, Skill acquisition, and Robot Navigation are applications of________

A)

Reinforcement Learning

B)

Supervised Learning: Classification

C)

Unsupervised Learning: Regression

D)

None of the above

Correct Answer : Reinforcement Learning

24 .

ML is a field of AI consisting of learning algorithms that?

A)

Improve their performance

B)

At executing some task

C)

Over time with experience

D)

All of the above

Correct Answer : All of the above

Explanation : ML is a field of AI consisting of learning algorithms that : Improve their performance (P), At executing some task (T), Over time with experience (E).

25 .

p → 0q is not a?

A)

hack clause

B)

horn clause

C)

system clause

D)

structural clause

Correct Answer : horn clause

Explanation : p → 0q is not a horn clause.

26 .

A model of language consists of the categories which does not include ________.

A)

Data units

B)

System Units

C)

Structural units.

D)

Empirical units

Correct Answer : Structural units.

27 .

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)

All of the above

Correct Answer : Normalize the data -> PCA -> training

Explanation : You need to always normalize the data first. If not, PCA or other techniques that are used to reduce dimensions will give different results.

28 .

Residual is

A)

Residual = Observed value – Predicted value

B)

Residual = Predicted value – Observed value

C)

Residual = Observed value + Predicted value

D)

None of the above

Correct Answer : Residual = Observed value – Predicted value

29 .

Machine Learning has various function representation, which of the following is not function of symbolic?

A)

Rules in first-order predicate logic

B)

Decision Trees

C)

Rules in propotional Logic

D)

Hidden-Markov Models (HMM)

Correct Answer : Hidden-Markov Models (HMM)

30 .

What is the most significant phase in a genetic algorithm?

A)

Mutation

B)

Crossover

C)

Selection

D)

Fitness function

Correct Answer : Crossover

Explanation : Crossover is the most significant phase in a genetic algorithm. For each pair of parents to be mated, a crossover point is chosen at random from within the genes.

31 .

A Machine Learning technique that helps in detecting the outliers in data.

A)

Clustering

B)

Classification

C)

Anomaly Detection

D)

All of the above

Correct Answer : Anomaly Detection

Explanation :

Outlier detection, often also referred to as Anomaly Detection, is the task to identify observations that deviate so much from other observations as to arouse suspicion that they were generated by a different mechanism (Hawkins, 1980).

In a frequently cited survey, Aggarwal (2015) categorized the types of outliers into three classes: (i) point outliers, (ii) collective outliers, and (iii) contextual outliers.

Specifically for temporal data, the review article by Gupta et al. (2014) highlights the two main types of outliers in time series data: (i) point outliers and (ii) subsequence outliers.

32 .

What is the disadvantage of decision trees?

A)

Factor analysis

B)

Decision trees are robust to outliers

C)

Decision trees are prone to be overfit

D)

All of the above

Correct Answer : Decision trees are prone to be overfit

33 .

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

A)

LIST(A,B)

B)

STACK(A,B)

C)

ARRAY(A,B)

D)

QUEUE(A,B)

Correct Answer : STACK(A,B)

34 .

A__________ begins by hypothesizing a sentence (the symbol S) and successively predicting lower level constituents until individual preterminal symbols are written.

A)

top-down parser

B)

bottow-up parser

C)

top parser

D)

bottom parser

Correct Answer : top-down parser

35 .

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

A)

Choose k to be 90% of m (k = 0.90*m, rounded to the opposite integer).

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)

Choose k to be the smallest value so that at least 99% of the varinace is retained.

Correct Answer : Choose k to be the smallest value so that at least 99% of the varinace is retained.

36 .

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

A)

Drop missing rows or columns

B)

Assign a unique category to missing values

C)

Replace missing values with mean/median/mode

D)

All of the above

Correct Answer : All of the above

37 .

Adding a non-important feature to a linear regression model may result in.

1) Increase in R-square

2) Decrease in R-square

A)

Only 1 is correct

B)

Only 2 is correct

C)

Both (a) and (b)

D)

None of the above

Correct Answer : Only 1 is correct

Explaination : After adding a feature in feature space, whether that feature is important or unimportant features the R-squared always increase.

38 .

What's the the hypothesis of logistic regression?

A)

to limit the cost function between 0 and +infinity

B)

to limit the cost function between -1 and 1

C)

to limit the cost function between 0 and 1

D)

to limit the cost function between -infinity and +infinity

Correct Answer : to limit the cost function between 0 and 1

39 .

Which of the followings are most widely used metrics and tools to assess a classification model?

A)

Confusion matrix

B)

Cost-sensitive accuracy

C)

Area under the ROC curve

D)

All of the above

Correct Answer : All of the above

40 .

Which one in the following is not Machine Learning disciplines?

A)

Physics

B)

Neurostatistics

C)

Information Theory

D)

Optimization Control

Correct Answer : Neurostatistics

41 .

Machine Learning has various search/ optimization algorithms, which of the following is not evolutionary computation?

A)

Neuro Evolution

B)

Perceptron

C)

Genetic Algorithm (GA)

D)

Genetic Programming (GP)

Correct Answer : Perceptron

42 .

What is the application of machine learning methods to a large database called?

A)

Data mining

B)

Internet of things

C)

Artificial intelligence

D)

Big data computing

Correct Answer : Data mining

Explanation : Application of machine learning methods to large databases is known as data mining.

43 .

Identify the type of learning in which labeled training data is used.

A)

Reinforcement learning

B)

Unsupervised learning

C)

Supervised learning

D)

Semi unsupervised learning

Correct Answer : Supervised learning

Explanation : Supervised learning uses labeled training data.

44 .

Identify the kind of learning algorithm for "facial identities for facial expressions".

A)

Prediction

B)

Generating patterns

C)

Recognition Patterns

D)

Recognizing anomalies

Correct Answer : Recognition Patterns

Explanation : For facial identities and facial expression, “Recognition Patterns” is used.

45 .

What kind of table compares classifications predicted by the model with the actual class labels?

A)

Residual plot

B)

Prediction plot

C)

Chaos table

D)

Confusion Matrix

Correct Answer : Confusion Matrix

46 .

What would make a robot intelligent?

A)

Responds to the Environment.

B)

It can jump 1.5 meters higher than humans.

C)

Calculates mathematical problems faster than Human Minds.

D)

Arrange actions according to the purpose and also has sensors and effectors.

Correct Answer : Arrange actions according to the purpose and also has sensors and effectors.

Explanation :

Intelligent robot has a well developed “artificial brain,” which can arrange actions according to the purpose and also has sensors and effectors. The research of the intelligent robot can be divided into four levels: basic frontier technology, common technology, key technology and equipment, and demonstration application. Among them, the basic frontier technology mainly involves the design of new robot mechanism, the theory and technology of intelligent development, and the research of new-generation robot verification platform such as mutual cooperation and human behavior enhancement.

Common technologies mainly include core components, robot-specific sensors, robot software, test/safety and reliability, and other key common technologies. Key technologies and equipment mainly include industrial robots, service robots, special environment service robots, and medical/rehabilitation robots.

47 .

Which of the following machine learning techniques helps in detecting the outliers in data?

A)

Clustering

B)

Anomaly detection

C)

Classification

D)

All of the Above

Correct Answer : Anomaly detection

Explanation : The machine learning algorithm which helps in detecting the outliers is known as anomaly detection.

48 .

Who is the father of machine learning?

A)

Geoffrey Everest Hinton

B)

Geoffrey Chaucer

C)

Geoffrey Hill

D)

None of the Above

Correct Answer : Geoffrey Everest Hinton

Explanation : The father of machine learning is Geoffrey Everest Hinton.

49 .

_______algorithms enable the computers to learn from data, and even improve themselves, without being explicitly programmed.

A)

Deep Learning

B)

Artificial Intelligence

C)

Machine Learning

D)

None of the Above

Correct Answer : Machine Learning

Explanation : Machine Learning algorithms enable the computers to learn from data, and even improve themselves, without being explicitly programmed.

50 .

Which supervised learning technique can process both numeric and categorical input attributes?

A)

Linear regression

B)

Bayes classifier

C)

Ogistic regression

D)

None of theAbove

Correct Answer : Linear regression

Explanation : Linear regression supervised learning technique can process both numeric and categorical input attributes.

51 .

Which of the following are common classes of problems in machine learning?

A)

Clustering

B)

Regression

C)

Classification

D)

All of the Above

Correct Answer : All of the Above

Explanation : All of the above are common classes of problems in machine learning.

52 .

Analysis of Machine Learning algorithm needs_____

A)

Statistical learning theory

B)

Computational learning theory

C)

Both (A) and (B)

D)

None of the Above

Correct Answer : Both (A) and (B)

Explanation : Analysis of Machine Learning algorithm needs Both Statistical learning theory & Computational learning theory.

53 .

What is the output of training process in machine learning?

A)

Accuracy

B)

Machine learning model

C)

Machine learning algorithm

D)

None of the Above

Correct Answer : Machine learning model

Explanation : Machine learning model is the output of training process in machine learning.

54 .

Which methods are used for the calibration in Supervised Learning?

A)

Platt Calibration

B)

Isotonic Regression

C)

Both (A) and (B)

D)

None of the Above

Correct Answer : Both (A) and (B)

Explanation : Both Platt Calibration & Isotonic Regression are used for the calibration in Supervised Learning.

55 .

Identify the successful applications of Machine Learning.

A)

Learning to recognize spoken words

B)

Learning to drive an autonomous vehicle

C)

Learning to classify new astronomical structures

D)

All of the Above

Correct Answer : All of the Above

Explanation :

* Learning to recognize spoken words

* Learning to drive an autonomous vehicle

* Learning to classify new astronomical structures

* Learning to play world-class backgammon etc,.

All of the above are applications of Machine Learning.

56 .

Bayes' theorem states that the relationship between the probability of the hypothesis before getting the evidence P(H) and the probability of the hypothesis after getting the evidence P(H∣E) is

A)

[P(E∣H)P(H)] / P(E)

B)

[P(E∣H) P(E) ] / P(H)

C)

[P(E) P(H) ] / P(E∣H)

D)

None of the Above

Correct Answer : [P(E∣H)P(H)] / P(E)

57 .

Machine learning is interested in the best hypothesis h from some space H, given observed training data D. Here best hypothesis means______

A)

Most general hypothesis

B)

Most probable hypothesis

C)

Most specific hypothesis

D)

None of the Above

Correct Answer : Most probable hypothesis

58 .

FIND-S algorithm ignores?

A)

Positive

B)

Negative

C)

Both (A) and (B)

D)

None of the Above

Correct Answer : Negative

Explanation :

* The FIND-S algorithm for finding the most specific hypothesis based on a given set of training data samples.

* In finds algorithm , we initialize hypothesis as an array of phi, thein in the first step we replace it with the first positive row of our dataset which is most specific hypothesis.

* In next step, we will traverse the dataset and check if the target value of dataset is positive or not, we will only consider positive value. if the value is positive we will traverse that row from start to end and check if any element matches with our respective hypothesis. if the element does not matches with the hypothesis, we will generalize the hypothesis and we will replace element in hypothesis with the dataset element .

* FIND-S algorithm ignores negative examples. – As long as the hypothesis space contains a hypothesis that describes the true target concept, and the training data contains no errors, ignoring negative examples does not cause any problem.

59 .

Select the correct definition of neuro software.

A)

It is software used by neurosurgeons

B)

It is software used to analyze neurons

C)

It is a powerful and easy neural network

D)

None of the Above

Correct Answer : It is a powerful and easy neural network

60 .

Choose the general limitations of the backpropagation rule among the following.

A)

Scaling

B)

Slow Convergence

C)

Local Minima Problem

D)

All of the Above

Correct Answer : All of the Above

Explanation : All of the above general limitations of the backpropagation rule.

61 .

The most common issue when using Machine Learning is____

A)

Poor Data Quality

B)

Inadequate Infrastructure

C)

Lack of skilled resources

D)

None of the Above

Correct Answer : Poor Data Quality

Explanation : The most common issue when using Machine Learning is a Poor Data Quality.

62 .

Choose the instance-based learner.

A)

Eager Learner

B)

Lazy Learner

C)

Both (A) and (B)

D)

None of the Above

Correct Answer : Lazy Learner

Explanation : Lazy Learner is an instance-based learner.

63 .

Identify the difficulties with the k-nearest neighbor algorithm.

A)

Curse of dimensionality

B)

Calculate the distance of the test case from all training cases

C)

Both (A) and (B)

D)

None of the Above

Correct Answer : Both (A) and (B)

Explanation : k-nearest neighbor algorithm is Both Curse of dimensionality & Calculate the distance of the test case from all training cases

64 .

Which of the following is an application of CBR?

A)

Planning

B)

Diagnosis

C)

Design

D)

None of the Above

Correct Answer : Design

Explanation : Design is an application of CBR.

The California Bearing Ratio(CBR) test is penetration test meant for the evaluation of subgrade strength of roads and pavements. The results obtained by these tests are used with the empirical curves to determine the thickness of pavement and its component layers.

65 .

A subset of machine learning that involves systems that think and learn like humans using artificial neural networks.

A)

Deep Learning

B)

Machine Learning

C)

Artificial Intelligence

D)

All of the Above

Correct Answer : Deep Learning

66 .

Which of the following method is used for trainControl resampling?

A)

svm

B)

bag32

C)

bag64

D)

repeatedcv

Correct Answer : repeatedcv

Explanation : repeatedcv stands for repeated cross-validation.

67 .

Radial basis function networks provide a global approximation to the target function, represented by____of many local kernel function.

A)

a parallel combination

B)

a linear combination

C)

a series combination

D)

a non linear combination

Correct Answer : a linear combination

68 .

Which of the following will be true about k in K-Nearest Neighbor in terms of Bias?

A)

When you decrease the k the bias will be increases

B)

When you increase the k the bias will be increases

C)

Both (A) and (B)

D)

None of the Above

Correct Answer : When you increase the k the bias will be increases

69 .

The prior goal of unsupervised learning model is to determine the ________ .

A)

Discuss

B)

Regression task

C)

Data patterns

D)

Classification task

Correct Answer : Data patterns

70 .

Machine Learning has various function representation, which of the following is not numerical functions?

A)

Case-based

B)

Neural Network

C)

Linear Regression

D)

Support Vector Machines

Correct Answer : Case-based

Machine Learning MCQs with Answers

Introduction :