730+ Machine Learning (ML) Solved MCQs

Machine learning is a subset of artificial intelligence that involves the use of algorithms and statistical models to enable a system to improve its performance on a specific task over time. In other words, machine learning algorithms are designed to allow a computer to learn from data, without being explicitly programmed.

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401.	Which of the following is true about Ridge 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» B. lasso regression uses subset selection of features

402.	Which of the following statement(s) can
A.	1 and 2
B.	1 and 3
C.	2 and 4
D.	none of the above
Answer» A. 1 and 2

403.	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 rate 2. It becomes slow when number of features is very large 3. No need to iterate
A.	1 and 2
B.	1 and 3.
C.	2 and 3.
D.	1,2 and 3.
Answer» D. 1,2 and 3.

404.	If two variables are correlated, is it necessary that they have a linear relationship?
A.	yes
B.	no
Answer» B. no

405.	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.
Answer» D. the relationship is symmetric between x and y in case of correlation but in case of regression it is not symmetric.

406.	Suppose you are using a Linear SVM classifier with 2 class classification
A.	yes
B.	no
Answer» A. yes

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

408.	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» A. the optimal hyperplane if exists, will be the one that completely separates the data

409.	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 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» A. we can still classify data correctly for given setting of hyper parameter c

410.	SVM can solvelinearand non- linearproblems
A.	true
B.	false
Answer» A. true

411.	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» A. true

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

416.	In SVM, Kernel function is used to map a lower dimensional data into a higher dimensional data.
A.	true
B.	false
Answer» A. true

418.	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
C.	both a and b
Answer» C. both a and b

420.	Which of the following isnotsupervised learning?
A.	��pca
B.	��decision tree
C.	��naive bayesian
D.	linerar regression
Answer» A. ��pca

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

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

415.	SVMalgorithmsusea set of mathematical functions that are defined as thekernel.
A.	true
B.	false
Answer» A. true

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

419.	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» C. both a and b

421.	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» B. semi-supervised

422.	In reinforcement learning, this feedback is
A.	overfitting
B.	overlearning
C.	reward
D.	none of above
Answer» C. reward

423.	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» A. deep learning

424.	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» C. modelfree

425.	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» B. deep learning

730+ Machine Learning (ML) Solved MCQs

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

Which of the following statement(s) can

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

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

Suppose you are using a Linear SVM classifier with 2 class classification

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

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

SVM can solvelinearand non- linearproblems

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.

Hyperplanes are boundaries that help classify the data points.

The of the hyperplane depends upon the number of features.

Hyperplanes are decision boundaries that help classify the data points.

SVMalgorithmsusea set of mathematical functions that are defined as thekernel.

In SVM, Kernel function is used to map a lower dimensional data into a higher dimensional data.

In SVR we try to fit the error within a

What is the purpose of performing cross- validation?

Which of the following is true about Naive Bayes ?

Which of the following isnotsupervised learning?

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.

In reinforcement learning, this feedback is

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

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

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

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

Correlated variables can have zero correlation coeffficient. True or False?

Suppose we fit Lasso Regression to a data set, which has 100 features (X1,X2X100). 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?

If Linear regression model perfectly first i.e., train error is zero, then

In syntax of linear model lm(formula,data,..), data refers to

Linear Regression is a supervised machine learning algorithm.

It is possible to design a Linear regression algorithm using a neural network?

Which of the following methods do we use to find the best fit line for data in Linear Regression?

Suppose you are training a linear regression model. Now consider these points.1. Overfitting is more likely if we have less data2. Overfitting is more likely when the hypothesis space is small.Which of the above statement(s) are correct?

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 dont have to choose the learning rate2. It becomes slow when number of features is very large3. No need to iterate

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

In a simple linear regression model (One independent variable), If we change the input variable by 1 unit. How much output variable will change?

Generally, which of the following method(s) is used for predicting continuous dependent variable?1. Linear Regression2. Logistic Regression

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

In a real problem, you should check to see if the SVM is separable and then include slack variables if it is not separable.

Which of the following are real world applications of the SVM?

100 people are at party. Given data gives information about how many wear pink or not, and if a man or not. Imagine a pink wearing guest leaves, was it a man?

For the given weather data, Calculate probability of playing

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

In reinforcement learning, this feedback is usually called as .

Which of the following sentence is correct?

Reinforcement learning is particularly

Which of the following sentence is FALSE regarding regression?

Which of the following method is used to find the optimal features for cluster analysis

scikit-learn also provides functions for creating dummy datasets from scratch:

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

In which of the following each categorical label is first turned into a positive integer and then transformed into a vector where only one feature is 1 while all the others are 0.

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.

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

Which of the following selects the best K high-score features.

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.

Which of the following method(s) does not have closed form solution for its coefficients?

Function used for linear regression in R

In the mathematical Equation of Linear Regression Y?=??1 + ?2X + ?, (?1, ?2) refers to

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 variables 2. I will start introducing polynomial degree variables 3. I will remove some variables

Problem:Players will play if weather is sunny. Is this statement is correct?

For the given weather data, Calculate probability of not playing

Suppose you have trained an SVM with linear decision boundary after training SVM, you correctly infer that your SVM model is under fitting.Which of the following option would you more likely to consider iterating SVM next time?

The minimum time complexity for training an SVM is O(n2). According to this fact, what sizes of datasets are not best suited for SVMs?

What do you mean by generalization error in terms of the SVM?

Support vectors are the data points that lie closest to the decision surface.

If I am using all features of my dataset and I achieve 100% accuracy on my training set, but ~70% on validation set, what should I look out for?

What is the purpose of performing cross- validation?

Linear SVMs have no hyperparameters that need to be set by cross-validation

For the given weather data, what is the probability that players will play if weather is sunny

100 people are at party. Given data gives information about how many wear pink or not, and if a man or not. Imagine a pink wearing guest leaves, what is the probability of being a man

Linear SVMs have no hyperparameters

What are the different Algorithm techniques in Machine Learning?

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

In reinforcement learning, this feedback is usually called as .

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

What does learning exactly mean?

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

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

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

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 variables
2. I will start introducing polynomial degree variables
3. I will remove some variables

During the last few years, many algorithms have been applied to deep
neural 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.

426.	If two variables are correlated, is it necessary that they have a linear relationship?
A.	yes
B.	no
Answer» B. no

427.	Correlated variables can have zero correlation coeffficient. True or False?
A.	true
B.	false
Answer» A. true

428.	Suppose we fit Lasso Regression to a data set, which has 100 features (X1,X2X100). 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
C.	can�t say
D.	none of these
Answer» B. it is more likely for x1 to be included in the model

429.	If Linear regression model perfectly first i.e., train error is zero, then
A.	test error is also always zero
B.	test error is non zero
C.	couldn�t comment on test error
D.	test error is equal to train error
Answer» C. couldn�t comment on test error

430.	In syntax of linear model lm(formula,data,..), data refers to
A.	matrix
B.	vector
C.	array
D.	list
Answer» B. vector

431.	Linear Regression is a supervised machine learning algorithm.
A.	true
B.	false
Answer» A. true

432.	It is possible to design a Linear regression algorithm using a neural network?
A.	true
B.	false
Answer» A. true

433.	Which of the following methods do we use to find the best fit line for data in Linear Regression?
A.	least square error
B.	maximum likelihood
C.	logarithmic loss
D.	both a and b
Answer» A. least square error

434.	Suppose you are training a linear regression model. Now consider these points.1. Overfitting is more likely if we have less data2. Overfitting is more likely when the hypothesis space is small.Which of the above statement(s) are correct?
A.	both are false
B.	1 is false and 2 is true
C.	1 is true and 2 is false
D.	both are true
Answer» C. 1 is true and 2 is false

435.	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 dont have to choose the learning rate2. It becomes slow when number of features is very large3. No need to iterate
A.	1 and 2
B.	1 and 3.
C.	2 and 3.
D.	1,2 and 3.
Answer» D. 1,2 and 3.