Machine Learning Quiz Questions

1. What is the role of a validation set in supervised learning?

view answer: C. To tune the model's hyperparameters and select the best model
2. What is underfitting in supervised learning?

view answer: C. A model that is too simple and does not capture the underlying patterns in the data
Explanation: Underfitting occurs when a model is too simple and cannot capture the underlying patterns in the data. This causes the model to perform poorly on both the training and testing data. Among the given options, option C correctly describes underfitting in supervised learning.
3. Which of the following is a common method for splitting data into training and testing sets?

view answer: C. k-fold cross-validation
4. Which of the following is an example of a supervised learning algorithm?

view answer: C. Decision tree
5. What is the purpose of using a loss function in supervised learning?

view answer: B. To measure the error between predicted and actual outputs
6. What is supervised learning?

view answer: C. A type of machine learning where the model is trained on labeled data
Explanation: Supervised learning is a type of machine learning where the model is trained on labeled data, meaning that the training data has both input features and corresponding output labels. The goal of supervised learning is to learn a function that maps the input to the output labels accurately, such that the function can be used to predict the output for new, unseen inputs. Among the given options, option C correctly describes supervised learning.
7. Which of the following is a supervised learning task?

view answer: C. Regression
Explanation: Supervised learning is a type of machine learning where the model is trained on labeled data, meaning that the training data has both input features and corresponding output labels. The goal of supervised learning is to learn a function that maps the input to the output labels accurately, such that the function can be used to predict the output for new, unseen inputs. Among the given options, option C correctly describes a supervised learning task.
8. What are the two main types of supervised learning problems?

view answer: B. Regression and classification
Explanation: Supervised learning problems can be broadly classified into two types: regression and classification. In regression problems, the goal is to predict a continuous output variable, such as the price of a house given its features. In classification problems, the goal is to predict a categorical output variable, such as whether an email is spam or not based on its content. Among the given options, only option B correctly describes the two main types of supervised learning problems.
9. What is the main goal of supervised learning?

view answer: A. To learn the best possible mapping from inputs to outputs
Explanation: The main goal of supervised learning is to learn a function that accurately maps the input to the output labels, such that it can be used to predict the output for new, unseen inputs. Among the given options, only option A correctly describes the main goal of supervised learning.
10. In supervised learning, what is a label?

view answer: C. The target or output variable associated with an instance
Explanation: In supervised learning, a label is the target or output variable associated with an instance. The input to the model consists of features or attributes that describe the instance, and the label is the output that we want the model to predict given the input. Among the given options, option C correctly describes what a label is in supervised learning.
11. What is overfitting in supervised learning?

view answer: B. A model that performs poorly on new, unseen data
Explanation: Overfitting occurs when a model is too complex and has learned the noise in the training data, instead of the underlying pattern. This causes the model to perform well on the training data, but poorly on new, unseen data. Among the given options, option D correctly describes overfitting in supervised learning.
12. Which of the following neural networks has a memory?

view answer: LSTM
13. Which is the following is true about neurons?

view answer: D. All of the above
14. Which of the following is an example of deep learning?

view answer: D. All of the above
15. Which of the following statement is not correct?

view answer: B. It can only work for a single input and a single output

© aionlinecourse.com All rights reserved.