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.

These multiple-choice questions (MCQs) are designed to enhance your knowledge and understanding in the following areas: Computer Science Engineering (CSE) .

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

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

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

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

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

706.	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

707.	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

708.	Which of the following is not supervised learning?
A.	PCA
B.	Decision Tree
C.	Naive Bayesian
D.	Linerar regression
Answer» A. PCA

709.	______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

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

711.	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

712.	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

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

715.	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
C.	Can’t say
D.	None of these
Answer» B. It is more likely for X1 to be included in the model

716.	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

713.	______ 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

717.	In syntax of linear model lm(formula,data,..), data refers to ______
A.	Matrix
B.	Vector
C.	Array
D.	List
Answer» B. Vector

718.	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.	1 and 2
B.	1 and 3.
C.	2 and 3.
D.	1,2 and 3.
Answer» D. 1,2 and 3.

719.	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.

720.	Which of the following are real world applications of the SVM?
A.	Text and Hypertext Categorization
B.	Image Classification
C.	Clustering of News Articles
D.	All of the above
Answer» D. All of the above

721.	Let’s say, you are working with categorical feature(s) and you have not looked at the distribution of the categorical variable in the test data. You want to apply one hot encoding (OHE) on the categorical feature(s). What challenges you may face if you have applied OHE on a categorical variable of train dataset?
A.	All categories of categorical variable are not present in the test dataset.
B.	Frequency distribution of categories is different in train as compared to the test dataset.
C.	Train and Test always have same distribution.
D.	Both A and B
Answer» D. Both A and B

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

723.	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» B. 2

724.	______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» A. Removing the whole line

725.	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» C. Both A & B

730+ Machine Learning (ML) Solved MCQs

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.

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

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

What is the purpose of performing cross-validation?

Which of the following is true about Naive Bayes ?

Which of the following is not supervised 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 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_____.

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?

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

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

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

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

_____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

______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________.

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.

Function used for linear regression in R is __________

In the mathematical Equation of Linear Regression Y = β1 + β2X + ϵ, (β1, β2) 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 variables2. I will start introducing polynomial degree variables3. I will remove some variables

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

The effectiveness of an SVM depends upon:

Suppose you are using RBF kernel in SVM with high Gamma value. What does this signify?

726.	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» C. In case of very large lambda; bias is high, variance is low

727.	Function used for linear regression in R is __________
A.	lm(formula, data)
B.	lr(formula, data)
C.	lrm(formula, data)
D.	regression.linear(formula, data)
Answer» A. lm(formula, data)

728.	In the mathematical Equation of Linear Regression Y = β1 + β2X + ϵ, (β1, β2) refers to __________
A.	(X-intercept, Slope)
B.	(Slope, X-Intercept)
C.	(Y-Intercept, Slope)
D.	(slope, Y-Intercept)
Answer» C. (Y-Intercept, Slope)

729.	We have been given a dataset with n records in which we have input attribute as x and output attribute as y. Suppose we use a linear regression method to model this data. To test our linear regressor, we split the data in training set and test set randomly. What do you expect will happen with bias and variance as you increase the size of training data?
A.	Bias increases and Variance increases
B.	Bias decreases and Variance increases
C.	Bias decreases and Variance decreases
D.	Bias increases and Variance decreases
Answer» D. Bias increases and Variance decreases

730.	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» A. 1 and 2

731.	The minimum time complexity for training an SVM is O(n2). According to this fact, what sizes of datasets are not best suited for SVM’s?
A.	Large datasets
B.	Small datasets
C.	Medium sized datasets
D.	Size does not matter
Answer» A. Large datasets

732.	The effectiveness of an SVM depends upon:
A.	Selection of Kernel
B.	Kernel Parameters
C.	Soft Margin Parameter C
D.	All of the above
Answer» D. All of the above

733.	We usually use feature normalization before using the Gaussian kernel in SVM. What is true about feature normalization? 1. We do feature normalization so that new feature will dominate other 2. Some times, feature normalization is not feasible in case of categorical variables3. Feature normalization always helps when we use Gaussian kernel in SVM
A.	1
B.	1 and 2
C.	1 and 3
D.	2 and 3
Answer» B. 1 and 2

734.	Suppose you are using RBF kernel in SVM with high Gamma value. What does this signify?
A.	The model would consider even far away points from hyperplane for modeling
B.	The model would consider only the points close to the hyperplane for modeling
C.	The model would not be affected by distance of points from hyperplane for modeling
D.	None of the above
Answer» B. The model would consider only the points close to the hyperplane for modeling