Machine Learning MCQs-1

 Machine Learning MCQs- 1 

Machine Learning 

1. Among the following option identify the one which is not a type of learning

  1. Semi unsupervised learning
  2. Supervised learning
  3. Unsupervised learning
  4. Reinforcement learning

Ans: 1

2. Identify the kind of learning algorithm for  “facial identities for facial expressions”.

  1. Prediction
  2. Recognition Pattern
  3. Recognition anomalies
  4. Generating Pattern
Ans: 2

3. What is the application of machine learning methods to a large database called?
  1. Big data computing
  2. Internet of things
  3. Data mining
  4. Artificial Intelligence
Ans: 3

4. Identify the type of learning in which labeled training data is used.

  1. Semi unsupervised learning
  2. Supervised learning
  3. Unsupervised learning
  4. Reinforcement learning
Ans: 2

5. What is the term known as on which the machine learning algorithms build a model based on sample data?
  1. Data training
  2. Training Data
  3. Transfer data
  4. None
Ans: 2

6. Machine learning is a subset of which of the following.
  1. Artificial  Intelligence
  2. Deep Learning
  3. Data learning
  4. None
Ans: 1

7. Which of the following are common classes of problems in machine learning?

  1. Regression
  2. Classification
  3. Clustering
  4. All of the above

Ans:4


8. Identify the successful applications of ML.

  1. Learning to classify new astronomical structures
  2. Learning to recognize spoken words
  3. Learning to drive an autonomous vehicle
  4. All of the above

Ans: 4

9. Analysis of ML algorithm needs

  1. Statistical learning theory
  2. Computational learning theory
  3. Both A and B
  4. None of the above

Ans: 3


10. What is true about Machine Learning?

  1. Machine Learning (ML) is the field of computer science
  2. ML is a type of artificial intelligence that extract patterns out of raw data by using an algorithm or method
  3. The main focus of ML is to allow computer systems learn from experience without being explicitly programmed or human intervention
  4. All of the above
Ans: 4

11. ML is a field of AI consisting of learning algorithms that?

  1. Improve their performance
  2. At executing some task
  3. Over time with experience
  4. All of the above

Ans: 4

12. How do you handle missing or corrupted data in a dataset?

  1. Drop missing rows or columns
  2. Replace missing values with mean/median/mode
  3. Assign a unique category to missing values
  4. All of the above
Ans: 4

13. What is the purpose of the cross-validation?
  1. To assess the predictive performance of the models
  2. To judge how the trained model performs outside the sample on test data
  3. Both A and B
  4. None
Ans: 3

14. What are the different types of attributes?
  1. Nominal
  2. Ordinal
  3. Special
  4. All of the above
Ans: 4

15. Examples of Nominal can be:
  1. ID Numbers, eye color, zip codes
  2. Rankings, taste of potato chips, grades, height
  3. Calendar dates, temperatures in celsius or Fahrenheit, phone numbers
  4. The temperature in Kelvin, length, time, counts
Ans: 1

16. Examples of Ordinal can be:
  1. ID Numbers, eye color, zip codes
  2. Rankings, taste of potato chips, grades, height
  3. Calendar dates, temperatures in Celsius or Fahrenheit, phone numbers
  4. Temperature in Kelvin, length, time, counts
Ans: 2

17. Examples of Interval can be:
  1. ID Numbers, eye color, zip codes
  2. Rankings, taste of potato chips, grades, height
  3. Calendar dates, temperatures in Celsius or Fahrenheit
  4. Temperature in Kelvin, length, time, counts
Ans: 4


18. What are some examples of data quality problems:
  1. Noise and outliers
  2. Duplicate data
  3. Missing values
  4. All of the Above
Ans: 4

19. Which Method is used for encoding the categorical variables?
  1. LabelEncoder 
  2. OneHotEncoder
  3. CategoryEncoder
  4. All of the Above 
Ans: 1
20. Why do we need feature transformation?
  1. Converting non-numeric features into numeric
  2. Resizing inputs to a fixed size
  3. Both A and B 
  4. None
Ans: 3

21. Which of the following is true about outliers -
  1. Data points that deviate a lot from normal observations
  2. Can reduce the accuracy of the model
  3. Both A and B 
  4. None
Ans: 3

22. Some of the Imputation methods are -
  1. Imputation with mean/median
  2. Imputing with random numbers 
  3. Imputing with one 
  4. All of the above
Ans: 1

23. The purpose of feature scaling is to -
  1. Accelerating the training time
  2. Getting better accuracy 
  3. Both A and B
  4. None
Ans: 3

24. In standardization, the features will be rescaled with 
  1. Mean 0 and Variance 0
  2. Mean 0 and Variance 1 
  3. Mean 1 and Variance 0
  4. Mean 1 and Variance 1 
Ans: 2

25. What is a Dummy Variable Trap?
  1. Multicollinearity among the dummy variables
  2. One variable predicts the value of other
  3. Both A and B
  4. None
Ans: 3

26. Which of the following(s) is/are features scaling techniques?
  1. Standardization
  2. Normalization
  3. Min-Max Scaling
  4. All of the Above
Ans: 4

27. The correct way of pre processing the data should be-
  1. Imputation ->feature scaling-> training
  2. Feature scaling->imputation->training
  3. Feature scaling->label encoding->training
  4. None
Ans: 1

28. What is the most common issue when using Machine Learning?

  1. Poor Data Quality
  2. Lack of skilled resources
  3. Inadequate Infrastructure
  4. None

Ans: 1

29. In machine learning, the module that must solve the given performance task is known as ---

  1. Critic
  2. Generalizer
  3. Performance system
  4. All of the above

Ans: 3

30. What is the output of training process in machine learning?

  1. Null
  2. Accuracy
  3. Machine learning model
  4. Machine learning algorithm

Ans: 3

31. ------ algorithms enable the computers to learn from data, and even improve themselves, without being explicitly programmed.

  1. Deep Learning
  2. Machine Learning
  3. Artificial Intelligence
  4. None

Ans: 2


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