## Machine Learning MCQs UNIT Wise, Final Year SPPU.

**Machine Learning** being the most prominent areas of the era finds its place in the curriculum of many universities or institutes, among which is **Savitribai Phule Pune University(SPPU)**.

Machine Learning subject, having subject no.:- **410250**, the first compulsory subject of **8 ^{th} semester **and has 3 credits in the course, according to the new credit system. This subject is the first compulsory subject that includes all the basics of this topic to its efficient algorithms. If any student develops interest in this subject, going through this course will be a good start.

This subject gives knowledge from the introduction of Machine Learning terminologies and types like supervised, unsupervised, etc. to its various techniques like clustering, classification, etc.

As we know, the syllabus of the upcoming final exams contains only the first four units of this course, so, the below-given MCQs cover the first 4 units of ML subject as:-

**Unit 1. Introduction to Machine Learning**

**Unit 2. Feature Selection**

**Unit 3. Regression**

**Unit 4. Naïve Bayes and Support Vector Machine**

So, here are the **MCQs on the subject Machine Learning** **from the course of ****Computer branch, ****SPPU**, which will clearly help you out on the upcoming exams.

## Machine Learning MCQs UNIT I

1. What is classification?

a) when the output variable is a category, such as “red” or “blue” or “disease” and “no disease”.

b) when the output variable is a real value, such as “dollars” or “weight”.**Ans: Solution A**

2. What is regression?

a) When the output variable is a category, such as “red” or “blue” or “disease” and “no disease”.

b) When the output variable is a real value, such as “dollars” or “weight”.**Ans: Solution B**

3. What is supervised learning?

a) All data is unlabelled and the algorithms learn to inherent structure from the input data

b) All data is labelled and the algorithms learn to predict the output from the input data

c) It is a framework for learning where an agent interacts with an environment and receives a reward for each interaction

d) Some data is labelled but most of it is unlabelled and a mixture of supervised and unsupervised techniques can be used.**Ans: Solution B**

4. What is Unsupervised learning?

a) All data is unlabelled and the algorithms learn to inherent structure from the input data

b) All data is labelled and the algorithms learn to predict the output from the input data

c) It is a framework for learning where an agent interacts with an environment and receives a reward for each interaction

d) Some data is labelled but most of it is unlabelled and a mixture of supervised and unsupervised techniques can be used.**Ans: Solution A**

5. What is Semi-Supervised learning?

a) All data is unlabelled and the algorithms learn to inherent structure from the input data

b) All data is labelled and the algorithms learn to predict the output from the input data

c) It is a framework for learning where an agent interacts with an environment and receives

a reward for each interaction

d) Some data is labelled but most of it is unlabelled and a mixture of supervised and

unsupervised techniques can be used.**Ans: Solution D**

6. What is Reinforcement learning?

a) All data is unlabelled and the algorithms learn to inherent structure from the input data

b) All data is labelled and the algorithms learn to predict the output from the input data

c) It is a framework for learning where an agent interacts with an environment and receives

a reward for each interaction

d) Some data is labelled but most of it is unlabelled and a mixture of supervised and

unsupervised techniques can be used.**Ans: Solution C**

7. Sentiment Analysis is an example of:

a)Regression,

b)Classification

c)Clustering

d)Reinforcement Learning

Options:

A. 1 Only

B. 1 and 2

C. 1 and 3

D. 1, 2 and 4**Ans : Solution D**

8. The process of forming general concept definitions from examples of concepts to be

learned.

a) Deduction

b) abduction

c) induction

d) conjunction**Ans : Solution C**

9. Computers are best at learning

a) facts.

b) concepts.

c) procedures.

d) principles.**Ans : Solution A**

10. Data used to build a data mining model.

a) validation data

b) training data

c) test data

d) hidden data**Ans : Solution B**

11. Supervised learning and unsupervised clustering both require at least one

a) hidden attribute.

b) output attribute.

c) input attribute.

d) categorical attribute.**Ans : Solution A**

12. Supervised learning differs from unsupervised clustering in that supervised learning requires

a) at least one input attribute.

b) input attributes to be categorical.

c) at least one output attribute.

d) output attributes to be categorical.**Ans : Solution B**

13. A regression model in which more than one independent variable is used to predict the

dependent variable is called

a) a simple linear regression model

b) a multiple regression models

c) an independent model

d) none of the above**Ans : Solution C**

14. A term used to describe the case when the independent variables in a multiple regression model

are correlated is

a) Regression

b) correlation

c) multicollinearity

d) none of the above**Ans : Solution C**

15. A multiple regression model has the form: y = 2 + 3×1 + 4×2. As x1 increases by 1 unit (holding x2 constant), y will

a) increase by 3 units

b) decrease by 3 units

c) increase by 4 units

d) decrease by 4 units**Ans : Solution C**

16. A multiple regression model has

a) only one independent variable

b) more than one dependent variable

c) more than one independent variable

d) none of the above**Ans : Solution B**

17. A measure of goodness of fit for the estimated regression equation is the

a) multiple coefficient of determination

b) mean square due to error

c) mean square due to regression

d) none of the above**Ans : Solution C**

18. The adjusted multiple coefficient of determination accounts for

a) the number of dependent variables in the model

b) the number of independent variables in the model

c) unusually large predictors

d) none of the above**Ans : Solution D**

19. The multiple coefficient of determination is computed by

a) dividing SSR by SST

b) dividing SST by SSR

c) dividing SST by SSE

d) none of the above**Ans : Solution C**

20. For a multiple regression model, SST = 200 and SSE = 50. The multiple coefficient of

determination is

a) 0.25

b) 4.00

c) 0.75

d) none of the above**Ans : Solution B**

21. A nearest neighbor approach is best used

a) with large-sized datasets.

b) when irrelevant attributes have been removed from the data.

c) when a generalized model of the data is desirable.

d) when an explanation of what has been found is of primary importance.**Ans : Solution B**

22. Another name for an output attribute.

a) predictive variable

b) independent variable

c) estimated variable

d) dependent variable**Ans : Solution B**

23. Classification problems are distinguished from estimation problems in that

a) classification problems require the output attribute to be numeric.

b) classification problems require the output attribute to be categorical.

c) classification problems do not allow an output attribute.

d) classification problems are designed to predict future outcome.**Ans : Solution C**

24. Which statement is true about prediction problems?

a) The output attribute must be categorical.

b) The output attribute must be numeric.

c) The resultant model is designed to determine future outcomes.

d) The resultant model is designed to classify current behavior.**Ans : Solution D**

25. Which statement about outliers is true?

a) Outliers should be identified and removed from a dataset.

b) Outliers should be part of the training dataset but should not be present in the test

data.

c) Outliers should be part of the test dataset but should not be present in the training

data.

d) The nature of the problem determines how outliers are used.**Ans : Solution D**

26. Which statement is true about neural network and linear regression models?

a) Both models require input attributes to be numeric.

b) Both models require numeric attributes to range between 0 and 1.

c) The output of both models is a categorical attribute value.

d) Both techniques build models whose output is determined by a linear sum of weighted

input attribute values.**Ans : Solution A**

27. Which of the following is a common use of unsupervised clustering?

a) detect outliers

b) determine a best set of input attributes for supervised learning

c) evaluate the likely performance of a supervised learner model

d) determine if meaningful relationships can be found in a dataset**Ans : Solution A**

28. The average positive difference between computed and desired outcome values.

a) root mean squared error

b) mean squared error

c) mean absolute error

d) mean positive error**Ans : Solution D**

29. Selecting data so as to assure that each class is properly represented in both the training and

test set.

a) cross validation

b) stratification

c) verification

d) bootstrapping**Ans : Solution B**

30. The standard error is defined as the square root of this computation.

a) The sample variance divided by the total number of sample instances.

b) The population variance divided by the total number of sample instances.

c) The sample variance divided by the sample mean.

d) The population variance divided by the sample mean.**Ans : Solution A**

31. Data used to optimize the parameter settings of a supervised learner model.

a) Training

b) Test

c) Verification

d) Validation**Ans : Solution D**

32. Bootstrapping allows us to

a) choose the same training instance several times.

b) choose the same test set instance several times.

c) build models with alternative subsets of the training data several times.

d) test a model with alternative subsets of the test data several times.**Ans : Solution A**

33. The correlation between the number of years an employee has worked for a company and the salary of the employee is 0.75. What can be said about employee salary and years worked?

a) There is no relationship between salary and years worked.

b) Individuals that have worked for the company the longest have higher salaries.

c) Individuals that have worked for the company the longest have lower salaries.

d) The majority of employees have been with the company a long time.

e) The majority of employees have been with the company a short period of time.**Ans : Solution B**

34. The correlation coefficient for two real-valued attributes is –0.85. What does this value tell you?

a) The attributes are not linearly related.

b) As the value of one attribute increases the value of the second attribute also increases.

c) As the value of one attribute decreases the value of the second attribute increases.

d) The attributes show a curvilinear relationship.**Ans : Solution C**

35. The average squared difference between classifier predicted output and actual output.

a) mean squared error

b) root mean squared error

c) mean absolute error

d) mean relative error**Ans : Solution A**

36. Simple regression assumes a __________ relationship between the input attribute and output

attribute.

a) Linear

b) Quadratic

c) reciprocal

d) inverse**Ans : Solution A**

37. Regression trees are often used to model _______ data.

a) Linear

b) Nonlinear

c) Categorical

d) Symmetrical**Ans : Solution B**

38. The leaf nodes of a model tree are

a) averages of numeric output attribute values.

b) nonlinear regression equations.

c) linear regression equations.

d) sums of numeric output attribute values.**Ans : Solution C**

39. Logistic regression is a ________ regression technique that is used to model data having a

_____outcome.

a) linear, numeric

b) linear, binary

c) nonlinear, numeric

d) nonlinear, binary**Ans : Solution D**

40. This technique associates a conditional probability value with each data instance.

a) linear regression

b) logistic regression

c) simple regression

d) multiple linear regression**Ans : Solution B**

41. This supervised learning technique can process both numeric and categorical input attributes.

a) linear regression

b) Bayes classifier

c) logistic regression

d) backpropagation learning**Ans : Solution A**

42. With Bayes classifier, missing data items are

a) treated as equal compares.

b) treated as unequal compares.

c) replaced with a default value.

d) ignored.**Ans : Solution B**

43. This clustering algorithm merges and splits nodes to help modify nonoptimal partitions.

a) agglomerative clustering

b) expectation maximization

c) conceptual clustering

d) K-Means clustering**Ans : Solution D**

44. This clustering algorithm initially assumes that each data instance represents a single cluster.

a) agglomerative clustering

b) conceptual clustering

c) K-Means clustering

d) expectation maximization**Ans : Solution C**

45. This unsupervised clustering algorithm terminates when mean values computed for the current iteration of the algorithm are identical to the computed mean values for the previous iteration.

a) agglomerative clustering

b) conceptual clustering

c) K-Means clustering

d) expectation maximization**Ans : Solution C**

46. Machine learning techniques differ from statistical techniques in that machine learning methods

a) typically assume an underlying distribution for the data.

b) are better able to deal with missing and noisy data.

c) are not able to explain their behavior.

d) have trouble with large-sized datasets.**Ans : Solution B**

## Machine Learning MCQs UNIT –II

1.True- False: Over fitting is more likely when you have huge amount of data to train?

A) TRUE

B) FALSE**Ans Solution: (B)**

With a small training dataset, it’s easier to find a hypothesis to fit the training data exactly i.e. over fitting.

2.What is pca.components_ in Sklearn?

A)Set of all eigen vectors for the projection space

B)Matrix of principal components

C)Result of the multiplication matrix

D)None of the above options**Ans A**

3.Which of the following techniques would perform better for reducing dimensions of a data

set?

A. Removing columns which have too many missing values

B. Removing columns which have high variance in data

C. Removing columns with dissimilar data trends

D. None of these**Ans Solution: (A) **If a columns have too many missing values, (say 99%) then we can remove such columns.

4.It is not necessary to have a target variable for applying dimensionality reduction

algorithms.

A. TRUE

B. FALSE**Ans Solution: (A)**

LDA is an example of supervised dimensionality reduction algorithm

5. PCA can be used for projecting and visualizing data in lower dimensions.

A. TRUE

B. FALSE**Ans Solution: (A)**

Sometimes it is very useful to plot the data in lower dimensions. We can take the first 2 principal components and then visualize the data using scatter plot.

6. The most popularly used dimensionality reduction algorithm is Principal Component Analysis (PCA). Which of the following is/are true about PCA?

1.PCA is an unsupervised method

2.It searches for the directions that data have the largest variance

3.Maximum number of principal components <= number of features

4.All principal components are orthogonal to each other

A. 1 and 2

B. 1 and 3

C. 2 and 3

D. All of the above**Ans D**

7. PCA works better if there is?

1.A linear structure in the data

2.If the data lies on a curved surface and not on a flat surface

3.If variables are scaled in the same unit

A. 1 and 2

B. 2 and 3

C. 1 and 3

D. 1 ,2 and 3**Ans Solution: (C)**

8. What happens when you get features in lower dimensions using PCA?

1.The features will still have interpretability

2.The features will lose interpretability

3.The features must carry all information present in data

4.The features may not carry all information present in data

A. 1 and 3

B. 1 and 4

C. 2 and 3

D. 2 and 4**Ans Solution: (D)**

When you get the features in lower dimensions then you will lose some information of data most of the times and you won’t be able to interpret the lower dimension data.

9. Which of the following option(s) is / are true?

1.You need to initialize parameters in PCA

2.You don’t need to initialize parameters in PCA

3.PCA can be trapped into local minima problem

4.PCA can’t be trapped into local minima problem

A. 1 and 3

B. 1 and 4

C. 2 and 3

D. 2 and 4**Ans Solution: (D)**

PCA is a deterministic algorithm which doesn’t have parameters to initialize and it doesn’t have local minima problem like most of the machine learning algorithms has.

10. What is of the following statement is true about t-SNE in comparison to PCA?

A. When the data is huge (in size), t-SNE may fail to produce better results.

B. T-NSE always produces better result regardless of the size of the data

C. PCA always performs better than t-SNE for smaller size data.

D. None of these**Ans Solution: (A)**

Option A is correct

11. [ True or False ] PCA can be used for projecting and visualizing data in lower dimensions.

A. TRUE

B. FALSE**Solution: (A)**

Sometimes it is very useful to plot the data in lower dimensions. We can take the first 2 principal components and then visualize the data using scatter plot.

12. A feature F1 can take certain value: A, B, C, D, E, & F and represents grade of students from a college.

1) Which of the following statement is true in following case?

A) Feature F1 is an example of nominal variable.

B) Feature F1 is an example of ordinal variable.

C) It doesn’t belong to any of the above category.

D) Both of these**Solution: (B)**

Ordinal variables are the variables which has some order in their categories. For example, grade A should be consider as high grade than grade B.

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

A) PCA

B) K-Means

C) None of the above**Solution: (A)**

A deterministic algorithm is that in which output does not change on different runs. PCA would give the same result if we run again, but not k-means

## Machine Learning MCQs UNIT –III

1. Which of the following methods do we use to best fit the data in Logistic Regression?

A) Least Square Error

B) Maximum Likelihood

C) Jaccard distance

D) Both A and B**Ans Solution: B**

2. Choose which of the following options is true regarding One-Vs-All method in Logistic

Regression.

A) We need to fit n models in n-class classification problem

B) We need to fit n-1 models to classify into n classes

C) We need to fit only 1 model to classify into n classes

D) None of these**Ans Solution: A**

3. Suppose, You applied a Logistic Regression model on a given data and got a training accuracy X and testing accuracy Y. Now, you want to add a few new features in the same data. Select the option(s) which is/are correct in such a case.

Note: Consider remaining parameters are same.

A) Training accuracy increases

B) Training accuracy increases or remains the same

C) Testing accuracy decreases

D) Testing accuracy increases or remains the same**Ans Solution: A and D**

Adding more features to model will increase the training accuracy because model has to

consider more data to fit the logistic regression. But testing accuracy increases if feature is found to be significant

4. Which of the following algorithms do we use for Variable Selection?

A) LASSO

B) Ridge

C) Both

D) None of these**Ans Solution: A**

In case of lasso we apply a absolute penality, after increasing the penality in lasso some of the coefficient of variables may become zero

5. Which of the following statement is true about outliers in Linear regression?

A) Linear regression is sensitive to outliers

B) Linear regression is not sensitive to outliers

C) Can’t say

D) None of these**Ans Solution: (A)**The slope of the regression line will change due to outliers in most of the cases. So Linear

Regression is sensitive to outliers.

6. 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**Ans Solution: (A)**

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

7. Which of the following is true about Residuals?

A) Lower is better

B) Higher is better

C) A or B depend on the situation

D) None of these**Ans Solution: (A)**

Residuals refer to the error values of the model. Therefore lower residuals are desired.

8. Suppose you plotted a scatter plot between the residuals and predicted values in linear

regression and you found that there is a relationship between them. Which of the following

conclusion do you make about this situation?

A) Since the there is a relationship means our model is not good

B) Since the there is a relationship means our model is good

C) Can’t say

D) None of these**Ans Solution: (A)**

There should not be any relationship between predicted values and residuals. If there exists any relationship between them, it means that the model has not perfectly captured the information in the data.

9. Suppose you have fitted a complex regression model on a dataset. Now, you are using Ridge regression with penalty x.

Choose the option which describes bias in best manner.

A) In case of very large x; bias is low

B) In case of very large x; bias is high

C) We can’t say about bias

D) None of these**Ans Solution: (B)**

If the penalty is very large it means model is less complex, therefore the bias would be high.

10. Which of the following option is true?

A) Linear Regression errors values has to be normally distributed but in case of Logistic

Regression it is not the case

B) Logistic Regression errors values has to be normally distributed but in case of Linear

Regression it is not the case

C) Both Linear Regression and Logistic Regression error values have to be normally distributed

D) Both Linear Regression and Logistic Regression error values have not to be normally

distributed**Ans Solution: A**

11. Suppose you have trained a logistic regression classifier and it outputs a new example x with

a prediction ho(x) = 0.2. This means

Our estimate for P(y=1 | x)

Our estimate for P(y=0 | x)

Our estimate for P(y=1 | x)

Our estimate for P(y=0 | x)**Ans Solution: B**

12. True-False: Linear Regression is a supervised machine learning algorithm.

A) TRUE

B) FALSE**Solution: (A)**

Yes, Linear regression is a supervised learning algorithm because it uses true labels for training. Supervised learning algorithm should have input variable (x) and an output variable (Y) for each example

13. True-False: Linear Regression is mainly used for Regression.

A) TRUE

B) FALSE**Solution: (A)**

Linear Regression has dependent variables that have continuous values.

14. True-False: It is possible to design a Linear regression algorithm using a neural network?

A) TRUE

B) FALSE**Solution: (A)**

True. A Neural network can be used as a universal approximator, so it can definitely implement a linear regression algorithm.

15. 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**Solution: (A)**

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

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

A) AUC-ROC

B) Accuracy

C) Logloss

D) Mean-Squared-Error**Solution: (D)**

Since linear regression gives output as continuous values, so in such case we use mean squared error metric to evaluate the model performance. Remaining options are use in case of a classification problem.

17. True-False: Lasso Regularization can be used for variable selection in Linear Regression.

A) TRUE

B) FALSE**Solution: (A)**

True, In case of lasso regression we apply absolute penalty which makes some of the coefficients zero.

18. Which of the following is true about Residuals ?

A) Lower is better

B) Higher is better

C) A or B depend on the situation

D) None of these**Solution: (A)**Residuals refer to the error values of the model. Therefore lower residuals are desired.

19. Suppose that we have N independent variables (X1,X2… Xn) and dependent variable is Y.Now Imagine that you are applying linear regression by fitting the best fit line using least square error on this data. You found that correlation coefficient for one of it’s variable(Say X1) with Y is -0.95.

Which of the following is true for X1?

A) Relation between the X1 and Y is weak

B) Relation between the X1 and Y is strong

C) Relation between the X1 and Y is neutral

D) Correlation can’t judge the relationship**Solution: (B)**

The absolute value of the correlation coefficient denotes the strength of the relationship.

Since absolute correlation is very high it means that the relationship is strong between X1 and Y.

20. Looking at above two characteristics, which of the following option is the correct for

Pearson correlation between V1 and V2? If you are given the two variables V1 and V2 and they are following below two characteristics.

1. If V1 increases then V2 also increases

2. If V1 decreases then V2 behavior is unknown

A) Pearson correlation will be close to 1

B) Pearson correlation will be close to -1

C) Pearson correlation will be close to 0

D) None of these**Solution: (D)**

We cannot comment on the correlation coefficient by using only statement 1. We need to

consider the both of these two statements. Consider V1 as x and V2 as |x|. The correlation

coefficient would not be close to 1 in such a case.

21. Suppose Pearson correlation between V1 and V2 is zero. In such case, is it right to

conclude that V1 and V2 do not have any relation between them?

A) TRUE

B) FALSE**Solution: (B)**

Pearson correlation coefficient between 2 variables might be zero even when they have a

relationship between them. If the correlation coefficient is zero, it just means that that they

don’t move together. We can take examples like y=|x| or y=x^2.

22. True- False: Overfitting is more likely when you have huge amount of data to train?

A) TRUE

B) FALSE**Solution: (B)**With a small training dataset, it’s easier to find a hypothesis to fit the training data exactly i.e. overfitting.

23. 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. Thers is no need to iterate

A) 1 and 2

B) 1 and 3

C) 2 and 3

D) 1,2 and 3**Solution: (D)**

Instead of gradient descent, Normal Equation can also be used to find coefficients.

__Question Context 24-26:__

Suppose you have fitted a complex regression model on a dataset. Now, you are using Ridge regression with penality x.

24. Choose the option which describes bias in best manner.

A) In case of very large x; bias is low

B) In case of very large x; bias is high

C) We can’t say about bias

D) None of these**Solution: (B)**If the penalty is very large it means model is less complex, therefore the bias would be high.

25. What will happen when you apply very large penalty?

A) Some of the coefficient will become absolute zero

B) Some of the coefficient will approach zero but not absolute zero

C) Both A and B depending on the situation

D) None of these**Solution: (B)**

In lasso some of the coefficient value become zero, but in case of Ridge, the coefficients become close to zero but not zero.

26. What will happen when you apply very large penalty in case of Lasso?

A) Some of the coefficient will become zero

B) Some of the coefficient will be approaching to zero but not absolute zero

C) Both A and B depending on the situation

D) None of these**Solution: (A)**

As already discussed, lasso applies absolute penalty, so some of the coefficients will become zero.

27. Which of the following statement is true about outliers in Linear regression?

A) Linear regression is sensitive to outliers

B) Linear regression is not sensitive to outliers

C) Can’t say

D) None of these**Solution: (A)**

The slope of the regression line will change due to outliers in most of the cases. So Linear

Regression is sensitive to outliers.

28. Suppose you plotted a scatter plot between the residuals and predicted values in linear

regression and you found that there is a relationship between them. Which of the following

conclusion do you make about this situation?

A) Since the there is a relationship means our model is not good

B) Since the there is a relationship means our model is good

C) Can’t say

D) None of these**Solution: (A)**

There should not be any relationship between predicted values and residuals. If there exists any relationship between them,it means that the model has not perfectly captured the information in the data.

**Q**__uestion Context 29-31:__

Suppose that you have a dataset D1 and you design a linear regression model of degree 3

polynomial and you found that the training and testing error is “0” or in another terms it

perfectly fits the data.

29. What will happen when you fit degree 4 polynomial in linear regression?

A) There are high chances that degree 4 polynomial will over fit the data

B) There are high chances that degree 4 polynomial will under fit the data

C) Can’t say

D) None of these**Solution: (A)**

Since is more degree 4 will be more complex(overfit the data) than the degree 3 model so it will again perfectly fit the data. In such case training error will be zero but test error may not be zero.

30. What will happen when you fit degree 2 polynomial in linear regression?

A) It is high chances that degree 2 polynomial will over fit the data

B) It is high chances that degree 2 polynomial will under fit the data

C) Can’t say

D) None of these**Solution: (B)**

If a degree 3 polynomial fits the data perfectly, it’s highly likely that a simpler model(degree 2 polynomial) might under fit the data.

31. In terms of bias and variance. Which of the following is true when you fit degree 2

polynomial?

A) Bias will be high, variance will be high

B) Bias will be low, variance will be high

C) Bias will be high, variance will be low

D) Bias will be low, variance will be low**Solution: (C)**

Since a degree 2 polynomial will be less complex as compared to degree 3, the bias will be high and variance will be low

**Qu****estion Context 32-33**:

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.

32. Now we increase the training set size gradually. As the training set size increases, what do you expect will happen with the mean training error?

A) Increase

B) Decrease

C) Remain constant

D) Can’t Say**Solution: (D)**

Training error may increase or decrease depending on the values that are used to fit the model. If the values used to train contain more outliers gradually, then the error might just increase.

33. 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

E) Can’t Say False**Solution: (D)**

As we increase the size of the training data, the bias would increase while the variance would decrease.

Question Context 34:

Consider the following data where one input(X) and one output(Y) is given

34. What would be the root mean square training error for this data if you run a Linear

Regression model of the form (Y = A0+A1X)?

A) Less than 0

B) Greater than zero

C) Equal to 0

D) None of these**Solution: (C)**

We can perfectly fit the line on the following data so mean error will be zero.

__Question Context 35-36:__

Suppose you have been given the following scenario for training and validation error for Linear Regression.

35. Which of the following scenario would give you the right hyper parameter?

A) 1

B) 2

C) 3

D) 4**Solution: (B)**

Option B would be the better option because it leads to less training as well as validation error.

36. Sup pose you got the tuned hyper parameters from the previous question. Now, Imagine

you want to add a variable in variable space such that this added feature is important. Which of the following thing would you observe in such case?

A) Training Error will decrease and Validation error will increase

B) Training Error will increase and Validation error will increase

C) Training Error will increase and Validation error will decrease

D) Training Error will decrease and Validation error will decrease

E) None of the above**Solution: (D)**

If the added feature is important, the training and validation error would decrease.

__Question Context 37-38:__

Suppose, you got a situation where you find that your linear regression model is under fitting

the data.

37. 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

A) 1 and 2

B) 2 and 3

C) 1 and 3

D) 1, 2 and 3**Solution: (A)**

In case of under fitting, you need to induce more variables in variable space or you can add

some polynomial degree variables to make the model more complex to be able to fir the data better.

38. Now situation is same as written in previous question(under fitting).Which of following

regularization algorithm would you prefer?

A) L1

B) L2

C) Any

D) None of these**Solution: (D)**

I won’t use any regularization methods because regularization is used in case of overfitting.

39. True-False: Is Logistic regression a supervised machine learning algorithm?

A) TRUE

B) FALSE**Solution: A**

True, Logistic regression is a supervised learning algorithm because it uses true labels for

training. Supervised learning algorithm should have input variables (x) and an target variable (Y) when you train the model .

40. True-False: Is Logistic regression mainly used for Regression?

A) TRUE

B) FALSE**Solution: B**

Logistic regression is a classification algorithm, don’t confuse with the name regression.

41. True-False: Is it possible to design a logistic regression algorithm using a Neural Network Algorithm?

A) TRUE

B) FALSE

**Solution: A**

True, Neural network is a is a universal approximator so it can implement linear regression

algorithm.

42. True-False: Is it possible to apply a logistic regression algorithm on a 3-class Classification

problem?

A) TRUE

B) FALSE**Solution: A**

Yes, we can apply logistic regression on 3 classification problem, We can use One Vs all method for 3 class classification in logistic regression.

43. Which of the following methods do we use to best fit the data in Logistic Regression?

A) Least Square Error

B) Maximum Likelihood

C) Jaccard distance

D) Both A and B**Solution: B**

Logistic regression uses maximum likely hood estimate for training a logistic regression.

44. Which of the following evaluation metrics can not be applied in case of logistic regression output to compare with target?

A) AUC-ROC

B) Accuracy

C) Logloss

D) Mean-Squared-Error**Solution: D**

Since, Logistic Regression is a classification algorithm so it’s output can not be real time value so mean squared error can not use for evaluating it

45. One of the very good methods to analyze the performance of Logistic Regression is AIC,

which is similar to R-Squared in Linear Regression. Which of the following is true about AIC?

A) We prefer a model with minimum AIC value

B) We prefer a model with maximum AIC value

C) Both but depend on the situation

D) None of these**Solution: A**

We select the best model in logistic regression which can least AIC.

46. [True-False] Standardisation of features is required before training a Logistic Regression.

A) TRUE

B) FALSE**Solution: B**

Standardization isn’t required for logistic regression. The main goal of standardizing features is to help convergence of the technique used for optimization.

47. Which of the following algorithms do we use for Variable Selection?

A) LASSO

B) Ridge

C) Both

D) None of these**Solution: A**

In case of lasso we apply a absolute penality, after increasing the penality in lasso some of the coefficient of variables may become zero

__Context: 48-49__

Consider a following model for logistic regression: P (y =1|x, w)= g(w0 + w1x)

where g(z) is the logistic function. In the above equation the P (y =1|x; w) , viewed as a function of x, that we can get by changing the parameters w.

48 What would be the range of p in such case?

A) (0, inf)

B) (-inf, 0 )

C) (0, 1)

D) (-inf, inf)**Solution: C**

For values of x in the range of real number from −∞ to +∞ Logistic function will give the output between (0,1)

49 In above question what do you think which function would make p between (0,1)?

A) logistic function

B) Log likelihood function

C) Mixture of both

D) None of them**Solution: A**

Explanation is same as question number 10

50. Suppose you have been given a fair coin and you want to find out the odds of getting heads. Which of the following option is true for such a case?

A) odds will be 0

B) odds will be 0.5

C) odds will be 1

D) None of these**Solution: C**

Odds are defined as the ratio of the probability of success and the probability of failure. So in case of fair coin probability of success is 1/2 and the probability of failure is 1/2 so odd would be 1

51. The logit function(given as l(x)) is the log of odds function. What could be the range of logit function in the domain x=[0,1]?

A) (– ∞ , ∞)

B) (0,1)

C) (0, ∞)

D) (- ∞, 0)**Solution: A**

For our purposes, the odds function has the advantage of transforming the probability function, which has values from 0 to 1, into an equivalent function with values between 0 and ∞. When we take the natural log of the odds function, we get a range of values from -∞ to ∞.

52. Which of the following option is true?

A) Linear Regression errors values has to be normally distributed but in case of Logistic Regression it is

not the case

B) Logistic Regression errors values has to be normally distributed but in case of Linear Regression it is

not the case

C) Both Linear Regression and Logistic Regression error values have to be normally distributed

D) Both Linear Regression and Logistic Regression error values have not to be normally distributed**Solution:A**

53. Which of the following is true regarding the logistic function for any value “x”?

Note:

Logistic(x): is a logistic function of any number “x”

Logit(x): is a logit function of any number “x”

Logit_inv(x): is a inverse logit function of any number “x”

A) Logistic(x) = Logit(x)

B) Logistic(x) = Logit_inv(x)

C) Logit_inv(x) = Logit(x)

D) None of these**Solution: B**

54. How will the bias change on using high(infinite) regularisation?

Suppose you have given the two scatter plot “a” and “b” for two classes( blue for positive and red for negative class). In scatter plot “a”, you correctly classified all data points using logistic regression ( black line is a decision boundary).

A) Bias will be high

B) Bias will be low

C) Can’t say

D) None of these**Solution: A**

Model will become very simple so bias will be very high.

55. Suppose, You applied a Logistic Regression model on a given data and got a training accuracy X and testing accuracy Y. Now, you want to add a few new features in the same data. Select the option(s) which is/are correct in such a case.

Note: Consider remaining parameters are same.

A) Training accuracy increases

B) Training accuracy increases or remains the same

C) Testing accuracy decreases

D) Testing accuracy increases or remains the same

**Solution: A and D**

Adding more features to model will increase the training accuracy because model has to consider more data to fit the logistic regression. But testing accuracy increases if feature is found to be significant

56. Choose which of the following options is true regarding One-Vs-All method in Logistic Regression.

A) We need to fit n models in n-class classification problem

B) We need to fit n-1 models to classify into n classes

C) We need to fit only 1 model to classify into n classes

D) None of these**Solution: A**

If there are n classes, then n separate logistic regression has to fit, where the probability of each category is predicted over the rest of the categories combined.

57. Below are two different logistic models with different values for β0 and β1

Which of the following statement(s) is true about β0 and β1 values of two logistics models (Green, Black)?

Note: consider Y = β0 + β1*X. Here, β0 is intercept and β1 is coefficient.

A) β1 for Green is greater than Black

B) β1 for Green is lower than Black

C) β1 for both models is same

D) Can’t Say**Solution: B**

β0 and β1: β0 = 0, β1 = 1 is in X1 color(black) and β0 = 0, β1 = −1 is in X4 color (green)

Context 58-60 Below are the three scatter plot(A,B,C left to right) and hand drawn decision boundaries for logistic regression.

58. Which of the following above figure shows that the decision boundary is overfitting the training data?

A) A

B) B

C) C

D)None of these**Solution: C**

Since in figure 3, Decision boundary is not smooth that means it will over-fitting the data.

59. What do you conclude after seeing this visualization?

1. The training error in first plot is maximum as compare to second and third plot.

2. The best model for this regression problem is the last (third) plot because it has minimum training error (zero).

3. The second model is more robust than first and third because it will perform best on unseen data.

4. The third model is overfitting more as compare to first and second.

5. All will perform same because we have not seen the testing data.

A) 1 and 3

B) 1 and 3

C) 1, 3 and 4

D) 5**Solution: C**

The trend in the graphs looks like a quadratic trend over independent variable X. A higher degree(Right graph) polynomial might have a very high accuracy on the train population but is expected to fail badly on test dataset. But if you see in left graph we will have training error maximum because it underfits the training data

60. Suppose, above decision boundaries were generated for the different value of regularization. Which of the above decision boundary shows the maximum regularization?

A) A

B) B

C) C

D) All have equal regularization**Solution: A**

Since, more regularization means more penality means less complex decision boundry that shows in first figure A.

61. What would do if you want to train logistic regression on same data that will take less time as well as give the comparatively similar accuracy(may not be same)?

Suppose you are using a Logistic Regression model on a huge dataset. One of the problem you may face on such huge data is that Logistic regression will take very long time to train.

A) Decrease the learning rate and decrease the number of iteration

B) Decrease the learning rate and increase the number of iteration

C) Increase the learning rate and increase the number of iteration

D) Increase the learning rate and decrease the number of iteration

**Solution: D**

If you decrease the number of iteration while training it will take less time for surly but will not give the same accuracy for getting the similar accuracy but not exact you need to increase the learning rate.

62. Which of the following image is showing the cost function for y =1.

Following is the loss function in logistic regression(Y-axis loss function and x axis log probability) for two class classification problem.

Note: Y is the target class

A) A

B) B

C) Both

D) None of these**Solution: A**

A is the true answer as loss function decreases as the log probability increases

63. Suppose, Following graph is a cost function for logistic regression.

Now, How many local minimas are present in the graph?

A) 1

B) 2

C) 3

D) 4**Solution: C**

There are three local minima present in the graph

64. Can a Logistic Regression classifier do a perfect classification on the below data?