Machine Learning Questions & Answers
11. Which of the following is a widely used and effective machine learning algorithm based on the idea of bagging?
A. Decision Tree
B. Regression
C. Classification
D. Random Forest
View Answer
Ans : D
Explanation: The Radom Forest algorithm builds an ensemble of Decision Trees, mostly trained with the bagging method.
12. To find the minimum or the maximum of a function, we set the gradient to zero because:
A. The value of the gradient at extrema of a function is always zero
B. Depends on the type of problem
C. Both A and B
D. None of the above
View Answer
Ans : A
Explanation: The gradient of a multivariable function at a maximum point will be the zero vector of the function, which is the single greatest value that the function can achieve.
13. Which of the following is a disadvantage of decision trees?
A. Factor analysis
B. Decision trees are robust to outliers
C. Decision trees are prone to be overfit
D. None of the above
View Answer
Ans : C
Explanation: Allowing a decision tree to split to a granular degree makes decision trees prone to learning every point extremely well to the point of perfect classification that is overfitting.
14. How do you handle missing or corrupted data in a dataset?
A. Drop missing rows or columns
B. Replace missing values with mean/median/mode
C. Assign a unique category to missing values
D. All of the above
View Answer
Ans : D
Explanation: All of the above techniques are different ways of imputing the missing values.
15. 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. None of the above
View Answer
Ans : A
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.
16. Which of the following statements about regularization is not correct?
A. Using too large a value of lambda can cause your hypothesis to underfit the data.
B. Using too large a value of lambda can cause your hypothesis to overfit the data
C. Using a very large value of lambda cannot hurt the performance of your hypothesis.
D. None of the above
View Answer
Ans : D
Explanation: A large value results in a large regularization penalty and therefore, a strong preference for simpler models, which can underfit the data.
17. 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
View Answer
Ans : C
Explanation: Lemmatization and stemming are the techniques of keyword normalization.
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
View Answer
Ans : D
Explanation: 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. Which of the following is a reasonable way to select the number of principal components "k"?
A. Choose k to be the smallest value so that at least 99% of the varinace is retained.
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. Use the elbow method.
View Answer
Ans : A
Explanation: This will maintain the structure of the data and also reduce its dimension.
20. What is a sentence parser typically used for?
A. It is used to parse sentences to check if they are utf-8 compliant.
B. It is used to parse sentences to derive their most likely syntax tree structures.
C. It is used to parse sentences to assign POS tags to all tokens.
D. It is used to check if sentences can be parsed into meaningful tokens.
View Answer
Ans : B
Explanation: Sentence parsers analyze a sentence and automatically build a syntax tree.
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