Data Science Interview Q’s — II
A walkthrough with/from the essentials of data science interviews.
Hi hey there, thanks for the continuous support for my previous articles. Today we will continue from our previous article “Data Science Interview Q’s — I” PART II, the commonly asked essential questions by the interviewers to understand the root level knowledge of DS rather than going for fancy advanced questions.
Remember “If your foundation Pillar is strong you can build anything”
1. Linear Regression is mainly used for Regression.
A) TRUE B) FALSE
Solution: (A) Linear Regression has dependent variables that have continuous values.
2. 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.
3. 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.
4. 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. The remaining options are used in case of a classification problem.
5. 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.
6. 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.
7. Suppose that we have N independent variables (X1,X2… Xn) and the dependent variable is Y. Now Imagine that you are applying linear regression by fitting the best fit line using the least square error on this data. You found that the correlation coefficient for one of its 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.
8. Looking at the 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 increases2. 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 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.
10. Suppose the Pearson correlation between V1 and V2 is zero. In such a 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 they don’t move together. We can take examples like y=|x| or y=x².
11. 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.
12. 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?
- We don’t have to choose the learning rate
- It becomes slow when the number of features is very large
3. There 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.
13. Suppose you have fitted a complex regression model on a dataset. Now, you are using Ridge regression with penality x.
Choose the option which describes bias in best manner.
A) In the case of very large x; bias is low
B) In the 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 the model is less complex, therefore the bias would be high
14. 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.
15. 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.
16. 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.
17. 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.
18. What will happen when you fit degree 4 polynomial in linear regression?
A) There are high chances that degree 4 polynomial will overfit the data
B) There are high chances that degree 4 polynomial will underfit 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 models so it will again perfectly fit the data. In such case, the training error will be zero but the test error may not be zero.
19. 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.
20. 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: Since a degree 2 polynomial will be less complex as compared to degree 3, the bias will be high and variance will be low.
21. Suppose l1, l2 and l3 are the three learning rates for A,B,C respectively. Which of the following is true about l1,l2 and l3?
A) l2 < l1 < l3B) l1 > l2 > l3
C) l1 = l2 = l3
D) None of these
Solution: (A) In case of high learning rate, step will be high, the objective function will decrease quickly initially, but it will not find the global minima and objective function starts increasing after a few iterations. In case of low learning rate, the step will be small. So the objective function will decrease slowly.
22. 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.
23. 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.
24. 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: We can perfectly fit the line on the following data so mean error will be zero.
25. Which of the following scenario would give you the right hyperparameter?
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 a validation error.
26. Suppose you got the tuned hyperparameters 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.
27. Suppose, you got a situation where you find that your linear regression model is under fitting the data.
In such a 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.
28. 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.
I hope you will find the questionnaires useful for your career and also the credit goes to aditya vidhya analytics from i was able to gather this set of interview questionnaires for you.
Next, we will walk through the clustering questionnaire in the next article Part III, which will surprise you!
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