Q. What is/are true about kernel in SVM?1. Kernel function map low dimensional data to high dimensional space2. It’s a similarity function
✅ Correct Answer: (C)
1 and 2
Machine Learning (ML) MCQs | Page - 61
Dear candidates you will find MCQ questions of Machine Learning (ML) here. Learn these questions and prepare yourself for coming examinations and interviews. You can check the right answer of any question by clicking on any option or by clicking view answer button.
Q. 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 it’s hyper parameter.What would happen when you use very small C (C~0)?
✅ Correct Answer: (A)
Misclassification would happen
Q. How do you handle missing or corrupted data in a dataset?
✅ Correct Answer: (D)
d. All of the above
Q. If there is only a discrete number of possible outcomes called _____.
✅ Correct Answer: (B)
Categories
Q. Some people are using the term ___ instead of prediction only to avoid the weird idea that machine learning is a sort of modern magic.
✅ Correct Answer: (A)
Inference
Q. The term _____ can be freely used, but with the same meaning adopted in physics or system theory.
✅ Correct Answer: (D)
Prediction
Q. Common deep learning applications / problems can also be solved using____
✅ Correct Answer: (B)
Classic approaches
Q. Which of the following statement(s) can be true post adding a variable in a linear regression model?
1. R-Squared and Adjusted R-squared both increase
2. R-Squared increases and Adjusted R-squared decreases
3. R-Squared decreases and Adjusted R-squared decreases
4. R-Squared decreases and Adjusted R-squared increases
✅ Correct Answer: (A)
1 and 2
Q. 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
✅ Correct Answer: (D)
1,2 and 3.
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