Evaluating Models Class 10 MCQ – The CBSE has changed the syllabus of Std. X. The MCQs are made based on the new syllabus and based on the New CBSE textbook, Sample paper and Board Paper. All the important Information are taken from the Artificial Intelligence Class X Textbook Based on CBSE Board Pattern.
Evaluating Models Class 10 MCQ
1. In a medical test for a rare disease, out of 1000 people tested, 50 actually have the disease while 950 do not. The test correctly identifies 40 out of the 50 people with the disease as positive, but it also wrongly identifies 30 of the healthy individuals as positive. What is the accuracy of the test?
a. 96%
b. 90%
c. 85%
d. 70%
2. A student solved 90 out of 100 questions correctly in a multiple-choice exam. What isthe error rate of the student’s answers?
a. 10%
b. 9%
c. 15%
d. 11%
3. In a spam email detection system, out of 1000 emails received, 300 are spam. The system correctly identifies 240 spam emails as spam, but it also marks 60 legitimate emails as spam. What is the precision of the system?
a. 80%
b. 70%
c. 75%
d. 90%
4. In a binary classification problem, a model predicts 70 instances as positive out of which 50 are actually positive. What is the recall of the model?
a. 50%
b. 70%
c. 80%
d. 100%
5. In a sentiment analysis task, a model correctly predicts 120 positive sentiments out of 200 positive instances. However, it also incorrectly predicts 40 negative sentiments as positive. What is the F1 score of the model?
a. 0.8
b. 0.75
c. 0.72
d. 0.82
6. A medical diagnostic test is designed to detect a certain disease. Out of 1000 people tested, 100 have the disease, and the test identifies 90 of them correctly. However, it also wrongly identifies 50 healthy people as having the disease. What is the precision of the test?
a. 90%
b. 80%
c. 70%
d. 64.3%
7. A teacher’s marks prediction system predicts the marks of a student as 75, but the actual marks obtained by the student are 80. What is the absolute error in the prediction?
a. 5
b. 10
c. 15
d. 20
8. The goal when evaluating an AI model is to:
a. Maximize error and minimize accuracy
b. Minimize error and maximize accuracy
c. Focus solely on the number of data points used
d. Prioritize the complexity of the model
9. A high F1 score generally suggests:
a. A significant imbalance between precision and recall
b. A good balance between precision and recall
c. A model that only performs well on specific data points
d. The need for more training data
10. How is the relationship between model performance and accuracy described?
a. Inversely proportional
b. Not related
c. Directly proportional
d. Randomly fluctuating
11. Assertion: Accuracy is an evaluation metric that allows you to measure the total number of predictions a model gets right.
Reasoning: The accuracy of the model and performance of the model is directly proportional, and hence better the performance of the model, the more accurate are the predictions.
Choose the correct option:
a. Both A and R are true and R is the correct explanation for A
b. Both A and R are true and R is not the correct explanation for A
c. A is True but R is False
d. A is false but R is True
12. Assertion: The sum of the values in a confusion matrix’s row represents the total number of instances for a given actual class.
Reasoning: This enables the calculation of class-specific metrics such as precision and recall, which are essential for evaluating a model’s performance across different classes.
Choose the correct option:
a. Both A and R are true and R is the correct explanation for A
b. Both A and R are true and R is not the correct explanation for A
c. A is True but R is False
d. A is false but R is True
13. The Indian Government banned a few apps stating – “servers in the hostile nation are receiving and using the acquired data improperly”. Which terminology suits best for this action?
a. AI Ethics
b. Data Privacy
c. AI Bias
d. AI Access
14. Which of the following is defined as the measure of balance between precision and recall ? (CBSE 2022 – 2023)
a. Accuracy
b. F1 Score
c. Reliability
d. Punctuality
15. Prediction and Reality can be easily mapped together with the help of :
a. Prediction
b. Reality
c. Accuracy
d. Confusion Matrix
16. F1 Score is the measure of the balance between
a. Accuracy and Precision
b. Precision and Recall
c. Recall and Accuracy
d. Recall and Reality
17. Statment1: The output given by the AI model is known as reality.
Statement2:The real scenario is known as Prediction.
a. Both Statement1 and Statement2 are correct
b. Both Statement1 and Statement2 are incorrect
c. Statement1 is correct but Statement2 is incorrect
d. Statement2 is correct but Statement1 is incorrect
18. Statement 1: Confusion matrix is an evaluation metric.
Statement 2 : Confusion Matrix is a record which helps in evaluation. (CBSE 2023 – 2024)
a. Both Statement 1 and Statement 2 are correct.
b. Both Statement 1 and Statement 2 are incorrect.
c. Statement 1 is correct and Statement 2 is incorrect.
d. Statement 2 is correct and Statement 1 is incorrect.
19. How many channels does a colour image have?
Show Answer ⟶20. Which feature of NLP helps in understanding the emotions of the people mentioned with the feedback?
a. Virtual Assistants
b. Sentiment Analysis
c. Text classification
d. Automatic Summarization
21. Which of the following represent a machine that is smart but not considered Artificial Intelligence (AI) enabled? (CBSE 2023 – 2024)
a. A robotic vacuum cleaner that can navigate and clean floors autonomously.
b. A chatbot that engages in natural language conversations and answers questions.
c. A smartphone with facial recognition for unlocking the device.
d. A digital alarm clock that rings at a set time every morning.
22. Which evaluation parameter takes into consideration all the correct predictions?
Show Answer ⟶23. Which of the following is incorrect?
i) Testing data is the one on which we train and fit our model basically to fit the parameters
ii) Training data is used only to assess performance of model
iii) Testing data is the unseen data for which predictions have to be made
a. i) and iii) only
b. i) and ii) only
c. ii) and iii) only
d. i), ii) and iii)
24. While evaluating a model’s performance, recall parameter considers (CBSE 2022 – 2023)
i) False positive
ii) True positive
iii) False negative
iv) True negative
Choose the correct option :
a. only i)
b. ii) and iii)
c. ii) and iv)
d. i) and iv)
25. Amazon had been working on a secret AI recruiting tool. The machine-learning specialists uncovered a big problem: their new recruiting engine did not like women. The system taught itself that male candidates were preferable. It penalized resumes that included the word “women”. This led to the failure of the tool. This is an example of
a. Data Privacy
b. AI access
c. AI Bias
d. Data Exploration
26. Which of the following represents an example of a recommendation system? (CBSE 2023 – 2024)
a. An online clothing store that offers a wide variety of clothing options.
b. A search engine that retrieves relevant web pages based on user queries.
c. An e-commerce website that displays customer reviews and ratings for products.
d. A music streaming platform that suggests songs and playlists based on user listening history.
27. Two conditions when prediction matches with the reality are true positive and __. (CBSE 2022 – 2023)
Show Answer ⟶28. The term Sentence Segmentation is
a. the whole corpus is divided into sentences
b. to undergo several steps to normalise the text to a lower level
c. in which each sentence is then further divided into tokens
d. the process in which the affixes of words are removed
29. Which of the following statements is true for the term Evaluation?
a. Helps in classifying the type and genre of a document.
b. It helps in predicting the topic for a corpus.
c. Helps in understanding the reliability of any AI model
d. Process to extract the important information out of a corpus.
30. Which of the following talks about how true the predictions are by any model ? (CBSE 2022 – 2023)
a. Accuracy
b. Reliability
c. Recall
d. F1 score
31. What is the primary need for evaluating an AI model’s performance in the AI Model Development process? (CBSE 2023 – 2024)
a. To increase the complexity of the model.
b. To visualize the data.
c. To assess how well the chosen model will work in future.
d. To reduce the amount of data used for training.
32. ____________is defined as the percentage of correct predictions out of all the observations.
a. Predictions
b. Accuracy
c. Reality
d. F1 Score
33. Name any two search engines. (CBSE 2023 – 2024)
Show Answer ⟶34. ___________ is the process of understanding the reliability of any AI model, based on outputs by feeding
test dataset into the model and comparing with actual answers.
a. Evaluation
b. Problem Scoping
c. Data acquisition
d. Data Exploration
35. If model will simply remember the whole training set, and will therefore always predict the correct label for any point in the training set. This is known as ____________.
a. Overfitting
b. Overriding
c. Over remembering
d. None of the above
36. The result of comparison between the prediction and reality can be recorded in what we call the __________.
a. Overfitting
b. Problem Scoping
c. Confusion Matrix
d. Data acquisition
37. The _____________ allows us to understand the prediction results.
a. Overfitting
b. Problem Scoping
c. Confusion Matrix
d. Data acquisition
38. _________ is defined as the percentage of correct predictions out of all the observations.
a. Overfitting
b. Accuracy
c. Confusion Matrix
d. Data acquisition
39. _______ is defined as the percentage of true positive cases versus all the cases where the prediction is true.
a. Overfitting
b. Accuracy
c. Precision
d. Data acquisition
40. ___________ can be defined as the fraction of positive cases that are correctly identified.
a. Recall
b. Accuracy
c. Precision
d. Data acquisition
41. ___________ can be defined as the measure of balance between precision and recall.
a. Recall
b. Accuracy
c. Precision
d. F1 Score
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