Machine Learning MCQ Questions: Practice and Test Your Knowledge Page-2 section-1

11.
What characterize unlabeled examples in machine learning

A. there is no prior knowledge

B. there is no confusing knowledge

C. there is prior knowledge

D. there is plenty of confusing knowledge

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12.
What characterize is hyperplance in geometrical model of machine learning?

A. a plane with 1 dimensional fewer than number of input attributes

B. a plane with 2 dimensional fewer than number of input attributes

C. a plane with 1 dimensional more than number of input attributes

D. a plane with 2 dimensional more than number of input attributes

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13.
Imagine a Newly-Born starts to learn walking. It will try to find a suitable policy to learn walking after repeated falling and getting up.specify what type of machine learning is best suited?

A. classification

B. regression

C. kmeans algorithm

D. reinforcement learning

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14.
What are the popular algorithms of Machine Learning?

A. decision trees and neural networks (back propagation)

B. probabilistic networks and nearest neighbor

C. support vector machines

D. all

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15.
A machine learning problem involves four attributes plus a class. The attributes have 3, 2, 2, and 2 possible values each. The class has 3 possible values. How many maximum possible different examples are there?

A. 12

B. 24

C. 48

D. 72

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16.
In machine learning, an algorithm (or learning algorithm) is said to be unstable if a small change in training data cause the large change in the learned classifiers. True or False: Bagging of unstable classifiers is a good idea

A. TRUE

B. FALSE

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17.
Which of the following is characteristic of best machine learning method ?

A. fast

B. accuracy

C. scalable

D. all above

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18.
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.

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Answer & Solution

Answer: Option A

Solution:

Machine learning techniques and statistical techniques are related fields, but they have distinct differences in their approaches and characteristics.

Option A: typically assume an underlying distribution for the data.
In statistical techniques, it is common to assume specific probability distributions for the data, and many statistical methods are based on these assumptions. In contrast, machine learning methods often do not make strong assumptions about the underlying data distribution. Instead, they focus on learning patterns and relationships directly from the data.

Option B: are better able to deal with missing and noisy data.
Machine learning methods often have techniques and algorithms designed to handle missing and noisy data effectively. They can adapt to imperfect data and still make predictions or classifications, whereas statistical methods may struggle with data quality issues.

Option C: are not able to explain their behavior.
This statement is not entirely accurate. Machine learning methods can be interpretable to some extent, and efforts have been made to develop explainable AI techniques. While some complex machine learning models may be less interpretable than traditional statistical models, they are not inherently incapable of explaining their behavior.

Option D: have trouble with large-sized datasets.
Machine learning methods are often well-suited for large-sized datasets, and many machine learning algorithms can scale to handle massive amounts of data. In fact, they are frequently used in big data analytics and large-scale applications.

In summary, the key differences between machine learning and statistical techniques lie in their approaches to data assumptions, handling missing/noisy data, and explainability. Therefore, the correct answer is Option A: typically assume an underlying distribution for the data.

19.
What is Model Selection in Machine Learning?

A. The process of selecting models among different mathematical models, which are used to describe the same data set

B. when a statistical model describes random error or noise instead of underlying relationship

C. Find interesting directions in data and find novel observations/ database cleaning

D. All above

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20.
Some people are using the term . . . . . . . . instead of prediction only to avoid the weird idea that machine learning is a sort of modern magic.

A. Inference

B. Interference

C. Accuracy

D. None of above

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Machine Learning MCQ Questions: Practice and Test Your Knowledge

11. What characterize unlabeled examples in machine learning

Answer & Solution

12. What characterize is hyperplance in geometrical model of machine learning?

Answer & Solution

13. Imagine a Newly-Born starts to learn walking. It will try to find a suitable policy to learn walking after repeated falling and getting up.specify what type of machine learning is best suited?

Answer & Solution

14. What are the popular algorithms of Machine Learning?

Answer & Solution

15. A machine learning problem involves four attributes plus a class. The attributes have 3, 2, 2, and 2 possible values each. The class has 3 possible values. How many maximum possible different examples are there?

Answer & Solution

16. In machine learning, an algorithm (or learning algorithm) is said to be unstable if a small change in training data cause the large change in the learned classifiers. True or False: Bagging of unstable classifiers is a good idea

Answer & Solution

17. Which of the following is characteristic of best machine learning method ?

Answer & Solution

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

Answer & Solution

19. What is Model Selection in Machine Learning?

Answer & Solution

20. Some people are using the term . . . . . . . . instead of prediction only to avoid the weird idea that machine learning is a sort of modern magic.

Answer & Solution

Read More Section(Machine Learning)

11.
What characterize unlabeled examples in machine learning

12.
What characterize is hyperplance in geometrical model of machine learning?

13.
Imagine a Newly-Born starts to learn walking. It will try to find a suitable policy to learn walking after repeated falling and getting up.specify what type of machine learning is best suited?

14.
What are the popular algorithms of Machine Learning?

15.
A machine learning problem involves four attributes plus a class. The attributes have 3, 2, 2, and 2 possible values each. The class has 3 possible values. How many maximum possible different examples are there?

16.
In machine learning, an algorithm (or learning algorithm) is said to be unstable if a small change in training data cause the large change in the learned classifiers. True or False: Bagging of unstable classifiers is a good idea

17.
Which of the following is characteristic of best machine learning method ?

18.
Machine learning techniques differ from statistical techniques in that machine learning methods

19.
What is Model Selection in Machine Learning?

20.
Some people are using the term . . . . . . . . instead of prediction only to avoid the weird idea that machine learning is a sort of modern magic.